Smart_Agriculture_System.ipynb

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    "# Smart Agriculture Advisory System\r\n",
    "\r\n",
    "## Overview\r\n",
    "The Smart Agriculture Advisory System is an application designed to provide farmers with personalized advice on crop management, pest control, and irrigation scheduling. By leveraging machine learning models, the system analyzes various environmental and soil parameters to recommend the most suitable crops for cultivation.\r\n",
    "\r\n",
    "## Dataset\r\n",
    "The dataset used for this project is sourced from Kaggle: [Crop Recommendation Dataset](https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset).\r\n",
    "\r\n",
    "### Features\r\n",
    "- **N (Nitrogen)**: Nitrogen content in the soil\r\n",
    "- **P (Phosphorus)**: Phosphorus content in the soil\r\n",
    "- **K (Potassium)**: Potassium content in the soil\r\n",
    "- **temperature**: Temperature in degree Celsius\r\n",
    "- **humidity**: Relative humidity in %\r\n",
    "- **ph**: pH value of the soil\r\n",
    "- **rainfall**: Rainfall in mm\r\n",
    "\r\n",
    "### Target\r\n",
    "- **label**: Type of crop to be grown\r\n",
    "\r\n",
    "## Installation\r\n",
    "\r\n",
    "### Prerequisites\r\n",
    "- [Anaconda](https://www.anaconda.com/products/individual#download-section): A distribution of Python and R for scientific computing and data science.\r\n",
    "\r\n",
    "### Steps\r\n",
    "\r\n",
    "1. **Install Anaconda**\r\n",
    "\r\n",
    "    Download and install Anaconda from the [official website](https://www.anaconda.com/products/individual#download-section) based on your operating system.\r\n",
    "\r\n",
    "2. **Clone the Repository**\r\n",
    "\r\n",
    "    ```bash\r\n",
    "    git clone https://github.com/your-username/smart-agriculture-advisory-system.git\r\n",
    "    cd smart-agriculture-advisory-system\r\n",
    "    ```\r\n",
    "\r\n",
    "3. **Create a Conda Environment**\r\n",
    "\r\n",
    "    ```bash\r\n",
    "    conda create --name smart-agriculture python=3.8\r\n",
    "    conda activate smart-agriculture\r\n",
    "    ```\r\n",
    "\r\n",
    "4. **Install Required Libraries**\r\n",
    "\r\n",
    "    ```bash\r\n",
    "    pip install -r requirements.txt\r\n",
    "    ```\r\n",
    "\r\n",
    "5. **Install Jupyter Notebook**\r\n",
    "\r\n",
    "    ```bash\r\n",
    "    conda install -c conda-forge notebook\r\n",
    "    ```\r\n",
    "\r\n",
    "## Usage\r\n",
    "\r\n",
    "1. **Run Jupyter Notebook**\r\n",
    "\r\n",
    "    ```bash\r\n",
    "    jupyter notebook\r\n",
    "    ```\r\n",
    "\r\n",
    "2. **Open the Notebook**\r\n",
    "\r\n",
    "    In the Jupyter Notebook interface, open `notebooks/Smart_Agriculture_Advisory_System.ipynb` to explore the analysis, model training, and predictions.\r\n",
    "\r\n",
    "## Project Structure\r\n",
    "\r\n",
    "- `data/`: Contains the dataset used for training and testing.\r\n",
    "- `notebooks/`: Jupyter notebooks for data analysis and model building.\r\n",
    "- `models/`: Saved machine learning models.\r\n",
    "- `scripts/`: Python scripts for data processing and model training.\r\n",
    "- `requirements.txt`: List of required Python libraries.\r\n",
    "\r\n",
    "## Model Details\r\n",
    "\r\n",
    "### Model Selection\r\n",
    "Several machine learning models were evaluated for this multi-class classification problem. The final model chosen for deployment is a Random Forest classifier due to its high accuracy and robustness.\r\n",
    "\r\n",
    "### Model Training\r\n",
    "The model was trained using the following steps:\r\n",
    "\r\n",
    "1. **Data Preprocessing**: Handling missing values, scaling features, and encoding categorical variables.\r\n",
    "2. **Feature Selection**: Identifying the most important features contributing to the prediction.\r\n",
    "3. **Model Training**: Training the Random Forest classifier with hyperparameter tuning using GridSearchCV.\r\n",
    "\r\n",
    "### Model Evaluation\r\n",
    "The model's performance was evaluated using accuracy, precision, recall, and F1-score. Cross-validation was also performed to ensure the model's robustness.\r\n",
    "\r\n",
    "## Contributing\r\n",
    "\r\n",
    "Contributions are welche [scikit-learn](https://scikit-learn.org/) community for providing excellent machine learning tools.\r\n"
   ]
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   "source": [
    "#Import all the tools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#Models for scikit learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Model evaluation\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.metrics import precision_score,recall_score,f1_score\n",
    "from sklearn.metrics import RocCurveDisplay"
   ]
  },
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   "cell_type": "code",
   "execution_count": 37,
   "id": "c6b035a0-fcb9-46ec-9441-50473ba437a2",
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    {
     "data": {
      "text/plain": [
       "(2200, 8)"
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     "execution_count": 37,
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   ],
   "source": [
    "df=pd.read_csv(\"Crop_recommendation.csv\")\n",
    "df.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 38,
   "id": "52700e16-da73-444e-824b-97b07bf9cdf7",
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       "label\n",
       "rice           100\n",
       "maize          100\n",
       "jute           100\n",
       "cotton         100\n",
       "coconut        100\n",
       "papaya         100\n",
       "orange         100\n",
       "apple          100\n",
       "muskmelon      100\n",
       "watermelon     100\n",
       "grapes         100\n",
       "mango          100\n",
       "banana         100\n",
       "pomegranate    100\n",
       "lentil         100\n",
       "blackgram      100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "kidneybeans    100\n",
       "chickpea       100\n",
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       "Name: count, dtype: int64"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"label\"].value_counts().plot(kind=\"bar\",color=[\"salmon\",\"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8c475e19-ab1e-4905-b3f3-b6752da40dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d6bb5180-51b6-4381-ae03-8e75b8d69f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.551818</td>\n",
       "      <td>53.362727</td>\n",
       "      <td>48.149091</td>\n",
       "      <td>25.616244</td>\n",
       "      <td>71.481779</td>\n",
       "      <td>6.469480</td>\n",
       "      <td>103.463655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.917334</td>\n",
       "      <td>32.985883</td>\n",
       "      <td>50.647931</td>\n",
       "      <td>5.063749</td>\n",
       "      <td>22.263812</td>\n",
       "      <td>0.773938</td>\n",
       "      <td>54.958389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>14.258040</td>\n",
       "      <td>3.504752</td>\n",
       "      <td>20.211267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.769375</td>\n",
       "      <td>60.261953</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>64.551686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>25.598693</td>\n",
       "      <td>80.473146</td>\n",
       "      <td>6.425045</td>\n",
       "      <td>94.867624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>28.561654</td>\n",
       "      <td>89.948771</td>\n",
       "      <td>6.923643</td>\n",
       "      <td>124.267508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>43.675493</td>\n",
       "      <td>99.981876</td>\n",
       "      <td>9.935091</td>\n",
       "      <td>298.560117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  \n",
       "count  2200.000000  2200.000000  \n",
       "mean      6.469480   103.463655  \n",
       "std       0.773938    54.958389  \n",
       "min       3.504752    20.211267  \n",
       "25%       5.971693    64.551686  \n",
       "50%       6.425045    94.867624  \n",
       "75%       6.923643   124.267508  \n",
       "max       9.935091   298.560117  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "89915831-8eaa-4cbd-ac6c-84f605bd97ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N\n",
       "22     44\n",
       "40     44\n",
       "27     42\n",
       "39     41\n",
       "31     41\n",
       "       ..\n",
       "136     2\n",
       "139     1\n",
       "135     1\n",
       "130     1\n",
       "46      1\n",
       "Name: count, Length: 137, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"N\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a5128704-960f-4294-8a95-3239e73d0f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ph\n",
       "6.502985    1\n",
       "6.640471    1\n",
       "6.251420    1\n",
       "6.274453    1\n",
       "6.135996    1\n",
       "           ..\n",
       "7.538631    1\n",
       "7.127064    1\n",
       "6.628265    1\n",
       "6.726469    1\n",
       "6.779833    1\n",
       "Name: count, Length: 2200, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ph\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d1b1368-4e74-482d-b1e3-e9209bc6646a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "#scatter with positive example\n",
    "plt.scatter(df.N[df.label==\"rice\"],df.ph[df.label==\"rice\"],c=\"salmon\");\n",
    "\n",
    "#scatter with negative example\n",
    "plt.scatter(df.N[df.label==\"jute\"],df.ph[df.label==\"jute\"],c=\"lightblue\");\n",
    "plt.title(\"Agriculture in N vs ph\")\n",
    "plt.xlabel(\"N\")\n",
    "plt.ylabel(\"ph\")\n",
    "plt.legend([\"Rice\", \"Jute\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6273ba04-8129-45b5-be56-ab659c2a4bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
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   "cell_type": "code",
   "execution_count": 48,
   "id": "6ad8cb5f-8cf8-45fe-9982-87177adb22f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"label\",axis=1)\n",
    "y=df[\"label\"]"
   ]
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   "execution_count": 49,
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       "        N   P   K  temperature   humidity        ph    rainfall\n",
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         rice\n",
       "1         rice\n",
       "2         rice\n",
       "3         rice\n",
       "4         rice\n",
       "         ...  \n",
       "2195    coffee\n",
       "2196    coffee\n",
       "2197    coffee\n",
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   "cell_type": "code",
   "execution_count": 51,
   "id": "d873d042-9dab-4745-906f-9fab1b493449",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
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       "        N    P    K  temperature   humidity        ph    rainfall\n",
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       "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
       "\n",
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    "X_train"
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  {
   "cell_type": "markdown",
   "id": "ac9187eb-59bd-4284-9401-b2640d0bcf83",
   "metadata": {},
   "source": [
    "# Models Used\n",
    "\n",
    "1) Logistic Regression\n",
    "2) K-Nearest Neighbours\n",
    "3) Random Forest Classifier\n",
    "4) Naive Byes\n",
    "5) GBM\n",
    "6) SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "32acea27-231d-4b2e-9025-1ad48fe553fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#put models in a dictionary\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "    \"K-Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"Support Vector Machine\": SVC(),\n",
    "    \"Gradient Boosting Machine\": GradientBoostingClassifier(),\n",
    "    \"Naive Bayes\": GaussianNB()\n",
    "}\n",
    "\n",
    "#create a function to fit and score models\n",
    "def fit_and_score(models,X_train,X_test,y_train,y_test):\n",
    "    np.random.seed(42)\n",
    "    model_scores={}\n",
    "    for name,model in models.items():\n",
    "        model.fit(X_train,y_train)\n",
    "        model_scores[name]=model.score(X_test,y_test)\n",
    "    return model_scores    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "906a01f8-5022-4e12-aeba-1e7156c76918",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saxen\\Smart_Agriculture_System\\env\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.9454545454545454,\n",
       " 'K-Nearest Neighbors': 0.9704545454545455,\n",
       " 'Random Forest': 0.9931818181818182,\n",
       " 'Support Vector Machine': 0.9613636363636363,\n",
       " 'Gradient Boosting Machine': 0.9818181818181818,\n",
       " 'Naive Bayes': 0.9954545454545455}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores=fit_and_score(models,X_train,X_test,y_train,y_test);\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ec06f3a8-6279-4a8e-82d5-5b10b64459c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"accuracy\"])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "06624bd6-54f9-439e-bf52-96612442741a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>K-Nearest Neighbors</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Support Vector Machine</th>\n",
       "      <th>Gradient Boosting Machine</th>\n",
       "      <th>Naive Bayes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.945455</td>\n",
       "      <td>0.970455</td>\n",
       "      <td>0.993182</td>\n",
       "      <td>0.961364</td>\n",
       "      <td>0.981818</td>\n",
       "      <td>0.995455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Logistic Regression  K-Nearest Neighbors  Random Forest  \\\n",
       "accuracy             0.945455             0.970455       0.993182   \n",
       "\n",
       "          Support Vector Machine  Gradient Boosting Machine  Naive Bayes  \n",
       "accuracy                0.961364                   0.981818     0.995455  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "840410a0-d3d1-4006-88f4-26ddf6bc3000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of differnt values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n_neighbors\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors=i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores list\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores list\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "80b9e005-525d-40b6-88ba-6e6d582b6cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.9892045454545455,\n",
       " 0.9886363636363636,\n",
       " 0.9863636363636363,\n",
       " 0.9897727272727272,\n",
       " 0.9852272727272727,\n",
       " 0.9875,\n",
       " 0.9835227272727273,\n",
       " 0.9857954545454546,\n",
       " 0.9823863636363637,\n",
       " 0.9835227272727273,\n",
       " 0.9784090909090909,\n",
       " 0.9806818181818182,\n",
       " 0.9789772727272728,\n",
       " 0.9801136363636364,\n",
       " 0.9767045454545454,\n",
       " 0.9795454545454545,\n",
       " 0.9738636363636364,\n",
       " 0.9761363636363637,\n",
       " 0.975]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1b962fa0-3005-4923-bc22-3f43b3b2993d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9659090909090909,\n",
       " 0.9590909090909091,\n",
       " 0.9704545454545455,\n",
       " 0.9681818181818181,\n",
       " 0.9704545454545455,\n",
       " 0.9681818181818181,\n",
       " 0.9681818181818181,\n",
       " 0.9613636363636363,\n",
       " 0.9659090909090909,\n",
       " 0.9590909090909091,\n",
       " 0.9659090909090909,\n",
       " 0.9636363636363636,\n",
       " 0.9659090909090909,\n",
       " 0.9613636363636363,\n",
       " 0.9613636363636363,\n",
       " 0.9590909090909091,\n",
       " 0.9590909090909091,\n",
       " 0.9522727272727273,\n",
       " 0.9522727272727273,\n",
       " 0.9545454545454546]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4b5b5776-75c6-4972-9ef3-6fe9cf66a468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 97.05%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1edd33c5-6e14-49a6-a0b6-ac8e74d4d946",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a hyperparamter grid for logisticregression\n",
    "log={\"C\":np.logspace(-4,4,20),\n",
    "\"solver\":[\"liblinear\"]\n",
    "}\n",
    "# Create a hyperparameter grid for RandomForestClassifier\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a50675e2-e303-4253-8302-9c91501fe891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=29.763514416313132, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=29.763514416313132, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for LogisticRegression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6819de34-3982-45dd-8417-3aa2b7c1447a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 29.763514416313132}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f8cf7e7b-d581-4c5f-8be1-d8fae322004a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9477272727272728"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d8a03c3b-ed9a-4476-aab5-9196ac85d2db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(min_samples_split=14, n_estimators=510)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(min_samples_split=14, n_estimators=510)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model for RandomForestClassifier()\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0ce943a8-e1d0-48de-86bc-083695583595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 510,\n",
       " 'min_samples_split': 14,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "63bec53b-0ff9-412e-8260-a5bcfb4a6b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9931818181818182"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1c32bf13-ba7a-4af0-a5ac-fe4eb2962aba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d8c36bbc-861a-4eeb-a35a-4bacf57857b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 32.90344562312671, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b008a748-10fa-4b11-acfd-981e8ff6ac70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9477272727272728"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf52a95-4fc4-4c8a-8003-377fd7fc57c8",
   "metadata": {},
   "source": [
    "# Evaluating our tuned machine learning classifier,beyond accuarcy\n",
    "Roc curve and UC curve\r\n",
    "confusion matrix\r\n",
    "classification report\r\n",
    "prescision\r\n",
    "f1-score\r\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "67926e31-0623-426f-9af7-7f27ed5668c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make predictions with tuned model\n",
    "y_preds=gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c6fbe737-393c-4216-8fc6-ae2b4eb89bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>101</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "      <td>29.494014</td>\n",
       "      <td>94.729813</td>\n",
       "      <td>6.185053</td>\n",
       "      <td>26.308209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>98</td>\n",
       "      <td>8</td>\n",
       "      <td>51</td>\n",
       "      <td>26.179346</td>\n",
       "      <td>86.522581</td>\n",
       "      <td>6.259336</td>\n",
       "      <td>49.430510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "      <td>49</td>\n",
       "      <td>43.360515</td>\n",
       "      <td>93.351916</td>\n",
       "      <td>6.941497</td>\n",
       "      <td>114.778071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>44</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>34.280461</td>\n",
       "      <td>90.555616</td>\n",
       "      <td>6.825371</td>\n",
       "      <td>98.540477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>200</td>\n",
       "      <td>22.914300</td>\n",
       "      <td>90.704756</td>\n",
       "      <td>5.603413</td>\n",
       "      <td>118.604465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>99</td>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>21.723831</td>\n",
       "      <td>80.238990</td>\n",
       "      <td>6.501698</td>\n",
       "      <td>277.962619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>67</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>22.727910</td>\n",
       "      <td>82.170688</td>\n",
       "      <td>7.300411</td>\n",
       "      <td>260.887506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>121</td>\n",
       "      <td>47</td>\n",
       "      <td>16</td>\n",
       "      <td>23.605640</td>\n",
       "      <td>79.295731</td>\n",
       "      <td>7.723240</td>\n",
       "      <td>72.498009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>116</td>\n",
       "      <td>52</td>\n",
       "      <td>19</td>\n",
       "      <td>22.942767</td>\n",
       "      <td>75.371706</td>\n",
       "      <td>6.114526</td>\n",
       "      <td>67.080226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "      <td>19.043805</td>\n",
       "      <td>33.106951</td>\n",
       "      <td>6.121667</td>\n",
       "      <td>155.370562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        N    P    K  temperature   humidity        ph    rainfall\n",
       "1451  101   17   47    29.494014  94.729813  6.185053   26.308209\n",
       "1334   98    8   51    26.179346  86.522581  6.259336   49.430510\n",
       "1761   59   62   49    43.360515  93.351916  6.941497  114.778071\n",
       "1735   44   60   55    34.280461  90.555616  6.825371   98.540477\n",
       "1576   30  137  200    22.914300  90.704756  5.603413  118.604465\n",
       "...   ...  ...  ...          ...        ...       ...         ...\n",
       "59     99   55   35    21.723831  80.238990  6.501698  277.962619\n",
       "71     67   45   38    22.727910  82.170688  7.300411  260.887506\n",
       "1908  121   47   16    23.605640  79.295731  7.723240   72.498009\n",
       "1958  116   52   19    22.942767  75.371706  6.114526   67.080226\n",
       "482     5   68   20    19.043805  33.106951  6.121667  155.370562\n",
       "\n",
       "[440 rows x 7 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "43905f37-7ae5-438e-a38f-f2ead73c5248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['muskmelon', 'watermelon', 'papaya', 'papaya', 'apple', 'mango',\n",
       "       'apple', 'mothbeans', 'mungbean', 'lentil', 'blackgram', 'coconut',\n",
       "       'pomegranate', 'jute', 'coconut', 'pomegranate', 'apple', 'cotton',\n",
       "       'rice', 'muskmelon', 'coffee', 'papaya', 'orange', 'papaya',\n",
       "       'chickpea', 'jute', 'mungbean', 'orange', 'pigeonpeas', 'rice',\n",
       "       'pomegranate', 'mothbeans', 'jute', 'lentil', 'jute', 'blackgram',\n",
       "       'jute', 'chickpea', 'chickpea', 'kidneybeans', 'rice', 'mango',\n",
       "       'blackgram', 'maize', 'mothbeans', 'maize', 'pigeonpeas',\n",
       "       'coconut', 'muskmelon', 'maize', 'blackgram', 'coffee', 'grapes',\n",
       "       'mungbean', 'coffee', 'kidneybeans', 'cotton', 'apple', 'banana',\n",
       "       'lentil', 'watermelon', 'coconut', 'lentil', 'orange', 'papaya',\n",
       "       'blackgram', 'orange', 'rice', 'muskmelon', 'pigeonpeas',\n",
       "       'muskmelon', 'coconut', 'jute', 'banana', 'blackgram', 'papaya',\n",
       "       'banana', 'cotton', 'watermelon', 'orange', 'coffee', 'chickpea',\n",
       "       'rice', 'mothbeans', 'orange', 'mango', 'coffee', 'mothbeans',\n",
       "       'blackgram', 'pomegranate', 'maize', 'mothbeans', 'cotton',\n",
       "       'papaya', 'jute', 'mothbeans', 'kidneybeans', 'coffee',\n",
       "       'blackgram', 'lentil', 'coconut', 'jute', 'orange', 'muskmelon',\n",
       "       'watermelon', 'kidneybeans', 'watermelon', 'banana', 'pigeonpeas',\n",
       "       'mothbeans', 'banana', 'rice', 'cotton', 'pomegranate',\n",
       "       'pigeonpeas', 'chickpea', 'maize', 'coconut', 'pomegranate',\n",
       "       'rice', 'pigeonpeas', 'grapes', 'blackgram', 'papaya', 'chickpea',\n",
       "       'blackgram', 'coconut', 'maize', 'banana', 'mothbeans', 'banana',\n",
       "       'kidneybeans', 'pomegranate', 'chickpea', 'coconut', 'orange',\n",
       "       'pigeonpeas', 'banana', 'banana', 'apple', 'kidneybeans',\n",
       "       'muskmelon', 'mungbean', 'mothbeans', 'coconut', 'maize', 'apple',\n",
       "       'coconut', 'kidneybeans', 'maize', 'mungbean', 'lentil',\n",
       "       'watermelon', 'mungbean', 'pigeonpeas', 'pigeonpeas', 'pigeonpeas',\n",
       "       'kidneybeans', 'maize', 'jute', 'chickpea', 'apple', 'orange',\n",
       "       'cotton', 'rice', 'coconut', 'grapes', 'lentil', 'watermelon',\n",
       "       'grapes', 'grapes', 'blackgram', 'pomegranate', 'chickpea',\n",
       "       'coconut', 'maize', 'lentil', 'grapes', 'watermelon', 'jute',\n",
       "       'coffee', 'coffee', 'kidneybeans', 'jute', 'mothbeans',\n",
       "       'kidneybeans', 'banana', 'kidneybeans', 'coconut', 'papaya',\n",
       "       'papaya', 'mungbean', 'mango', 'pomegranate', 'watermelon',\n",
       "       'kidneybeans', 'maize', 'apple', 'blackgram', 'chickpea', 'grapes',\n",
       "       'grapes', 'banana', 'cotton', 'rice', 'pomegranate', 'mungbean',\n",
       "       'banana', 'jute', 'mungbean', 'maize', 'chickpea', 'chickpea',\n",
       "       'chickpea', 'apple', 'rice', 'kidneybeans', 'papaya', 'coffee',\n",
       "       'blackgram', 'kidneybeans', 'mango', 'mango', 'coconut', 'papaya',\n",
       "       'apple', 'chickpea', 'pomegranate', 'chickpea', 'muskmelon',\n",
       "       'apple', 'muskmelon', 'muskmelon', 'mango', 'mango', 'cotton',\n",
       "       'coconut', 'pomegranate', 'rice', 'muskmelon', 'coffee', 'papaya',\n",
       "       'lentil', 'maize', 'mango', 'muskmelon', 'blackgram', 'mungbean',\n",
       "       'coffee', 'grapes', 'coconut', 'cotton', 'pigeonpeas', 'rice',\n",
       "       'apple', 'pomegranate', 'coffee', 'chickpea', 'cotton', 'jute',\n",
       "       'mango', 'banana', 'papaya', 'apple', 'chickpea', 'rice',\n",
       "       'pigeonpeas', 'mothbeans', 'mungbean', 'jute', 'pomegranate',\n",
       "       'grapes', 'mothbeans', 'papaya', 'cotton', 'coconut', 'cotton',\n",
       "       'chickpea', 'banana', 'rice', 'coconut', 'coffee', 'mango',\n",
       "       'muskmelon', 'apple', 'banana', 'pigeonpeas', 'blackgram',\n",
       "       'orange', 'chickpea', 'watermelon', 'banana', 'apple', 'chickpea',\n",
       "       'coffee', 'pigeonpeas', 'orange', 'apple', 'coconut', 'papaya',\n",
       "       'watermelon', 'mothbeans', 'rice', 'chickpea', 'pomegranate',\n",
       "       'chickpea', 'papaya', 'lentil', 'apple', 'pomegranate', 'chickpea',\n",
       "       'mango', 'chickpea', 'pomegranate', 'watermelon', 'kidneybeans',\n",
       "       'mungbean', 'muskmelon', 'watermelon', 'kidneybeans', 'muskmelon',\n",
       "       'mango', 'rice', 'blackgram', 'chickpea', 'banana', 'lentil',\n",
       "       'pigeonpeas', 'papaya', 'kidneybeans', 'mungbean', 'coconut',\n",
       "       'cotton', 'grapes', 'apple', 'lentil', 'banana', 'jute', 'apple',\n",
       "       'pomegranate', 'apple', 'mungbean', 'muskmelon', 'coffee',\n",
       "       'coffee', 'pigeonpeas', 'jute', 'kidneybeans', 'banana', 'maize',\n",
       "       'jute', 'cotton', 'pigeonpeas', 'mango', 'kidneybeans', 'jute',\n",
       "       'watermelon', 'blackgram', 'maize', 'rice', 'mothbeans',\n",
       "       'kidneybeans', 'mango', 'cotton', 'papaya', 'mothbeans', 'cotton',\n",
       "       'mungbean', 'orange', 'rice', 'blackgram', 'mungbean', 'coffee',\n",
       "       'banana', 'pigeonpeas', 'papaya', 'apple', 'pomegranate', 'maize',\n",
       "       'mango', 'coconut', 'apple', 'lentil', 'jute', 'mothbeans',\n",
       "       'lentil', 'coconut', 'blackgram', 'jute', 'mungbean', 'cotton',\n",
       "       'watermelon', 'apple', 'grapes', 'coconut', 'grapes', 'watermelon',\n",
       "       'rice', 'mango', 'mango', 'coffee', 'pomegranate', 'banana',\n",
       "       'grapes', 'jute', 'orange', 'cotton', 'mango', 'papaya',\n",
       "       'muskmelon', 'mothbeans', 'jute', 'chickpea', 'mothbeans',\n",
       "       'pomegranate', 'watermelon', 'mothbeans', 'cotton', 'papaya',\n",
       "       'watermelon', 'mothbeans', 'rice', 'coconut', 'mothbeans',\n",
       "       'mothbeans', 'maize', 'rice', 'maize', 'coconut', 'orange',\n",
       "       'pomegranate', 'kidneybeans', 'watermelon', 'mungbean',\n",
       "       'blackgram', 'rice', 'rice', 'cotton', 'cotton', 'pigeonpeas'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e58e199a-0723-4b51-991c-ba067e25b104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 18  0  0  0  0  0  0  0  2  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 26  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 26  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0 19  0  0  0  0  0  0  0  0  1  0  0  2  0]\n",
      " [ 0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  4  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  1  1  0 22  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  1 18  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  3  0]\n",
      " [ 0  0  2  0  0  0  0  0  1  1  0  0  0  0  0  0  0  0 19  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0 22  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0 18  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19]]\n"
     ]
    }
   ],
   "source": [
    "#confusion matrix\n",
    "print(confusion_matrix(y_test,y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "256674d6-7241-48e0-b484-22affdc6cc45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True,\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "\n",
    "plot_conf_mat(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f6e75e90-8ec4-4e00-8019-c8f1957ab1fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        23\n",
      "      banana       1.00      1.00      1.00        21\n",
      "   blackgram       0.90      0.90      0.90        20\n",
      "    chickpea       1.00      1.00      1.00        26\n",
      "     coconut       1.00      0.96      0.98        27\n",
      "      coffee       0.94      1.00      0.97        17\n",
      "      cotton       0.81      1.00      0.89        17\n",
      "      grapes       1.00      1.00      1.00        14\n",
      "        jute       0.86      0.83      0.84        23\n",
      " kidneybeans       0.95      1.00      0.98        20\n",
      "      lentil       0.79      1.00      0.88        11\n",
      "       maize       0.94      0.81      0.87        21\n",
      "       mango       1.00      1.00      1.00        19\n",
      "   mothbeans       0.96      0.92      0.94        24\n",
      "    mungbean       1.00      0.95      0.97        19\n",
      "   muskmelon       1.00      1.00      1.00        17\n",
      "      orange       1.00      1.00      1.00        14\n",
      "      papaya       0.91      0.87      0.89        23\n",
      "  pigeonpeas       1.00      0.83      0.90        23\n",
      " pomegranate       1.00      0.96      0.98        23\n",
      "        rice       0.78      0.95      0.86        19\n",
      "  watermelon       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           0.95       440\n",
      "   macro avg       0.95      0.95      0.95       440\n",
      "weighted avg       0.95      0.95      0.95       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "24397731-52c3-4b41-95ea-a9267b2545ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 32.90344562312671, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3255dbd0-4527-4f7a-be1f-2421bbcd94e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=32.90344562312671,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "28174a31-e671-4610-8e50-d0e2daf49a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97272727, 0.95681818, 0.96363636, 0.95227273, 0.95909091])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "cv_acc = cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\")\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "51188726-3441-4d27-af5d-bec43bf01eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9609090909090909"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_acc = np.mean(cv_acc)\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3658d3bf-4de3-404b-af3f-d23cbca33339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc},\n",
    "                          index=[0])\n",
    "\n",
    "cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n",
    "                      legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8092bbad-4ded-4a8a-a3ce-077c526dcbac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8279dab8-ef87-4ebd-ab3e-fad237c9c93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=32.90344562312671, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=32.90344562312671, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=32.90344562312671, solver='liblinear')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of LogisticRegression\n",
    "clf = LogisticRegression(C=32.90344562312671,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1048d215-e16f-4eb4-959c-919a8d1deba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.56985499e-02,  3.54425604e-02,  1.16611826e-01,\n",
       "        -7.84441036e-02,  3.02452586e-01, -6.81984904e+00,\n",
       "         1.11207036e-01],\n",
       "       [ 1.49997405e-01,  4.16456412e-01, -1.65789389e-01,\n",
       "         1.16734034e-01,  1.34127392e-01, -6.16415616e+00,\n",
       "        -9.16738521e-03],\n",
       "       [ 2.89060008e-02,  1.26786722e-01, -1.90635102e-01,\n",
       "         3.96612993e-01, -5.74788431e-03,  4.96734451e-01,\n",
       "        -2.96895210e-02],\n",
       "       [ 1.67452493e-01, -6.39032999e-02,  2.93030001e-01,\n",
       "         3.72534991e-02, -8.29361050e-01,  1.01054873e-01,\n",
       "        -1.55644192e-02],\n",
       "       [-1.09005947e-01, -2.75788771e-01, -2.66044612e-02,\n",
       "         2.90540731e-01,  5.02910840e-01, -7.93248443e+00,\n",
       "         9.37748236e-02],\n",
       "       [ 2.97276388e-01, -4.81108455e-01, -1.93953564e-01,\n",
       "         1.77218421e-01, -6.40572313e-01,  2.50157536e+00,\n",
       "         1.04985913e-01],\n",
       "       [ 2.42021310e-01, -3.28529039e-02, -5.32628794e-01,\n",
       "        -1.90103779e-01,  1.98535372e-01, -5.26228356e-01,\n",
       "        -6.14442674e-02],\n",
       "       [ 1.29747002e-03,  5.45566072e-02,  2.89091302e-01,\n",
       "         2.94548174e-02, -1.71282170e-01, -4.29008699e-03,\n",
       "        -5.74047879e-01],\n",
       "       [ 2.07243118e-02,  7.42008785e-03, -3.59977396e-03,\n",
       "        -5.62371567e-02,  2.82691153e-02,  7.23284768e-01,\n",
       "         1.74555766e-02],\n",
       "       [ 1.09741420e-01,  1.02720108e+00, -1.14128356e+00,\n",
       "        -7.10840135e-01, -1.87401152e+00,  5.22701069e+00,\n",
       "        -5.85574824e-02],\n",
       "       [-5.10571436e-02,  3.10779610e-01, -1.81416550e-01,\n",
       "        -5.17017190e-01,  6.28370226e-02,  8.55813262e-01,\n",
       "        -2.40974775e-01],\n",
       "       [ 3.75795279e-02, -1.20171387e-03, -1.13861458e-01,\n",
       "        -2.05531643e-01, -1.78226508e-02, -8.75073314e-01,\n",
       "        -1.46164937e-02],\n",
       "       [-9.84710346e-02, -4.50412135e-01,  1.55808802e-01,\n",
       "         1.06799992e+00, -3.54685617e-01, -1.12051532e+00,\n",
       "        -3.55762956e-03],\n",
       "       [-1.53025717e-02, -1.13402596e-01, -2.05959197e-01,\n",
       "         3.76627216e-01, -1.99641930e-01,  8.22170565e-01,\n",
       "        -1.49701663e-01],\n",
       "       [-9.48129252e-02,  1.85401778e-02, -4.28310723e-01,\n",
       "         8.90831886e-02,  5.73706114e-01, -1.15740486e+00,\n",
       "        -2.87208727e-01],\n",
       "       [ 2.11094804e-02, -3.83282849e-02,  1.11425389e-01,\n",
       "         9.78993837e-02,  3.02298678e-01, -2.48154388e-02,\n",
       "        -1.04031816e+00],\n",
       "       [-5.72335279e-02, -4.39449163e-01, -2.09726640e+00,\n",
       "        -4.60318966e-02,  4.88877397e-01,  2.92381294e-01,\n",
       "         2.20254084e-02],\n",
       "       [ 6.33108436e-03,  7.41849022e-02, -4.13878549e-02,\n",
       "         2.53182180e-01,  2.75445974e-01,  5.95154364e-01,\n",
       "         8.71422201e-03],\n",
       "       [-6.55129178e-02,  1.71687294e-01, -3.56451273e-01,\n",
       "         3.12141054e-01,  1.49199000e-02, -1.68948857e+00,\n",
       "         6.70731746e-02],\n",
       "       [-9.85763062e-02, -2.09875284e-01,  1.09234672e-01,\n",
       "        -1.61244773e-01,  6.18950706e-02, -1.91023760e-01,\n",
       "        -3.23461006e-02],\n",
       "       [ 8.66319326e-02,  4.28420592e-02, -9.19121801e-03,\n",
       "        -6.15431084e-01,  9.91673933e-02, -2.10777082e+00,\n",
       "         1.08822900e-01],\n",
       "       [ 6.32246064e-02, -1.48947712e-01,  7.66664024e-02,\n",
       "        -4.70814333e-01, -1.41401261e-02,  5.95346406e-02,\n",
       "        -1.35976320e-02]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "48109bee-8904-41f7-a865-8cef639e7262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N': -0.07569854990100175,\n",
       " 'P': 0.03544256041455715,\n",
       " 'K': 0.11661182600508674,\n",
       " 'temperature': -0.07844410358638174,\n",
       " 'humidity': 0.3024525861463832,\n",
       " 'ph': -6.819849042621763,\n",
       " 'rainfall': 0.11120703560878842}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4988565c-1953-47ff-8f9f-2a1f545f51b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1fdd3bd9-0abb-4cb9-836b-25e0e20a15e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>label</th>\n",
       "      <th>apple</th>\n",
       "      <th>banana</th>\n",
       "      <th>blackgram</th>\n",
       "      <th>chickpea</th>\n",
       "      <th>coconut</th>\n",
       "      <th>coffee</th>\n",
       "      <th>cotton</th>\n",
       "      <th>grapes</th>\n",
       "      <th>jute</th>\n",
       "      <th>kidneybeans</th>\n",
       "      <th>...</th>\n",
       "      <th>mango</th>\n",
       "      <th>mothbeans</th>\n",
       "      <th>mungbean</th>\n",
       "      <th>muskmelon</th>\n",
       "      <th>orange</th>\n",
       "      <th>papaya</th>\n",
       "      <th>pigeonpeas</th>\n",
       "      <th>pomegranate</th>\n",
       "      <th>rice</th>\n",
       "      <th>watermelon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "label  apple  banana  blackgram  chickpea  coconut  coffee  cotton  grapes  \\\n",
       "N                                                                            \n",
       "0          3       0          0         0        4       0       0       1   \n",
       "1          2       0          0         0        3       0       0       1   \n",
       "2          6       0          0         0        2       0       0       1   \n",
       "3          1       0          0         0        2       0       0       2   \n",
       "4          0       0          0         0        0       0       0       2   \n",
       "..       ...     ...        ...       ...      ...     ...     ...     ...   \n",
       "134        0       0          0         0        0       0       2       0   \n",
       "135        0       0          0         0        0       0       1       0   \n",
       "136        0       0          0         0        0       0       2       0   \n",
       "139        0       0          0         0        0       0       1       0   \n",
       "140        0       0          0         0        0       0       3       0   \n",
       "\n",
       "label  jute  kidneybeans  ...  mango  mothbeans  mungbean  muskmelon  orange  \\\n",
       "N                         ...                                                  \n",
       "0         0            2  ...      3          1         1          0       4   \n",
       "1         0            1  ...      3          0         2          0       3   \n",
       "2         0            1  ...      4          2         3          0       0   \n",
       "3         0            2  ...      2          3         0          0       0   \n",
       "4         0            1  ...      2          4         5          0       3   \n",
       "..      ...          ...  ...    ...        ...       ...        ...     ...   \n",
       "134       0            0  ...      0          0         0          0       0   \n",
       "135       0            0  ...      0          0         0          0       0   \n",
       "136       0            0  ...      0          0         0          0       0   \n",
       "139       0            0  ...      0          0         0          0       0   \n",
       "140       0            0  ...      0          0         0          0       0   \n",
       "\n",
       "label  papaya  pigeonpeas  pomegranate  rice  watermelon  \n",
       "N                                                         \n",
       "0           0           1            3     0           0  \n",
       "1           0           3            1     0           0  \n",
       "2           0           1            2     0           0  \n",
       "3           0           3            3     0           0  \n",
       "4           0           1            6     0           0  \n",
       "..        ...         ...          ...   ...         ...  \n",
       "134         0           0            0     0           0  \n",
       "135         0           0            0     0           0  \n",
       "136         0           0            0     0           0  \n",
       "139         0           0            0     0           0  \n",
       "140         0           0            0     0           0  \n",
       "\n",
       "[137 rows x 22 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"N\"], df[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ae33934d-9538-41b9-a30f-fcf5efcb2357",
   "metadata": {},
   "outputs": [
    {
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       "<p>2200 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "label     apple  banana  blackgram  chickpea  coconut  coffee  cotton  grapes  \\\n",
       "ph                                                                              \n",
       "3.504752      0       0          0         0        0       0       0       0   \n",
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       "\n",
       "label     jute  kidneybeans  ...  mango  mothbeans  mungbean  muskmelon  \\\n",
       "ph                           ...                                          \n",
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       "\n",
       "label     orange  papaya  pigeonpeas  pomegranate  rice  watermelon  \n",
       "ph                                                                   \n",
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       "9.926212       0       0           0            0     0           0  \n",
       "9.935091       0       0           0            0     0           0  \n",
       "\n",
       "[2200 rows x 22 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"ph\"], df[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8b9fb937-bc0a-4c60-89f9-b7f238440c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['smart-agriculture.joblib']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "from joblib import dump, load\n",
    "\n",
    "# Save a model to file\n",
    "dump(gs_log_reg,filename=\"smart-agriculture.joblib\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6d991c72-8aae-4a8d-aac5-242f1b4898f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(gs_log_reg,open(\"smart-agripkl.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3c96a596-26dc-485d-8950-1561a76f979b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe33bc51-aba7-46ba-bd7e-5e5079096cfc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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