Flask Intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76861234",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install flask\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06cb1f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flask import Flask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d1ca7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# >>> import numpy as np\n",
    "# >>> from sklearn.linear_model import LinearRegression\n",
    "#-----------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# reg = LinearRegression()\n",
    "---------- creating the instance for your Linear Regression Class\n",
    "# reg.fit(X, y)\n",
    "---------- .fit is one of the fuinction provided by linear regression\n",
    "\n",
    "\n",
    "# This for your ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b45630a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app '__main__' (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: This is a development server. Do not use it in a production deployment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " * Running on http://127.0.0.1:5001/ (Press CTRL+C to quit)\n",
      "127.0.0.1 - - [07/Aug/2022 08:15:44] \"GET / HTTP/1.1\" 404 -\n",
      "127.0.0.1 - - [07/Aug/2022 08:17:20] \"GET /Gaju HTTP/1.1\" 200 -\n"
     ]
    }
   ],
   "source": [
    "# Intialize the Flask in the same way\n",
    "app = Flask(__name__) #dir struct is required and __name__ take care of your dir struct\n",
    "# created a instance so that we can use any functionality of flask\n",
    "\n",
    "@app.route(\"/<name>\")# It binds the url with its below function\n",
    "def hello(name):\n",
    "    return \"<Html><h1>Welcome \"+name+\" to our first Deployment Session C35\"\n",
    "\n",
    "\n",
    "app.run(port=5001)# debug= True it does not work in jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4035b36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec28aa25",
   "metadata": {},
   "source": [
    "# Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "418ddaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score  interview_score  salary\n",
       "0        NaN         8.0                9   50000\n",
       "1        NaN         8.0                6   45000\n",
       "2       five         6.0                7   60000\n",
       "3        two        10.0               10   65000\n",
       "4      seven         9.0                6   70000\n",
       "5      three         7.0               10   62000\n",
       "6        ten         NaN                7   72000\n",
       "7     eleven         7.0                8   80000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/ingledarshan/upGrad_Darshan/main/hiring.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba101fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am creating a dummy project project so that we can deploy this\n",
    "# Salary prediction\n",
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96846580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47eae195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience         2\n",
       "test_score         1\n",
       "interview_score    0\n",
       "salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6185413",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.experience.fillna(0,inplace=True)\n",
    "df.test_score.fillna(df.test_score.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae605bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience         0\n",
       "test_score         0\n",
       "interview_score    0\n",
       "salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c17377e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>7.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score  interview_score  salary\n",
       "0          0    8.000000                9   50000\n",
       "1          0    8.000000                6   45000\n",
       "2       five    6.000000                7   60000\n",
       "3        two   10.000000               10   65000\n",
       "4      seven    9.000000                6   70000\n",
       "5      three    7.000000               10   62000\n",
       "6        ten    7.857143                7   72000\n",
       "7     eleven    7.000000                8   80000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da043608",
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_dict(x):\n",
    "    dict={\n",
    "        \"five\":5,\n",
    "        \"two\":2,\n",
    "        \"seven\":7,\n",
    "        \"three\":3,\n",
    "        \"ten\":10,\n",
    "        \"eleven\":11,\n",
    "        0:0\n",
    "    }\n",
    "    return dict[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85a926d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.experience = df.experience.apply(conv_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c349f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>7.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score  salary\n",
       "0           0    8.000000                9   50000\n",
       "1           0    8.000000                6   45000\n",
       "2           5    6.000000                7   60000\n",
       "3           2   10.000000               10   65000\n",
       "4           7    9.000000                6   70000\n",
       "5           3    7.000000               10   62000\n",
       "6          10    7.857143                7   72000\n",
       "7          11    7.000000                8   80000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74739b9e",
   "metadata": {},
   "source": [
    "# ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c60e5e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>experience</th>\n",
       "      <th>test_score</th>\n",
       "      <th>interview_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>7.857143</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score  interview_score\n",
       "0           0    8.000000                9\n",
       "1           0    8.000000                6\n",
       "2           5    6.000000                7\n",
       "3           2   10.000000               10\n",
       "4           7    9.000000                6\n",
       "5           3    7.000000               10\n",
       "6          10    7.857143                7\n",
       "7          11    7.000000                8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af9715e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50000\n",
       "1    45000\n",
       "2    60000\n",
       "3    65000\n",
       "4    70000\n",
       "5    62000\n",
       "6    72000\n",
       "7    80000\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:,-1]\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4bf58a5",
   "metadata": {},
   "source": [
    "# I am not doing train and test split here, but in real ML model make sure you do it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a71f8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20f986b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bb8b37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b5b63ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52313.61238494, 45722.68644263, 58231.95591138, 63991.7318464 ,\n",
       "       67429.06277517, 61080.55179794, 75922.72532666, 79307.67351488])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90b44287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53290.89255945])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c2086ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53290.89255945])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33a4b89",
   "metadata": {},
   "source": [
    "# Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5f27416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(lr,open(\"lr_c35.pkl\",'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c1f09fa",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_18984/3800227743.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'lr' is not defined"
     ]
    }
   ],
   "source": [
    "lr.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aab0a3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "lr = pickle.load(open(\"lr_c35.pkl\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3419928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53290.89255945])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d877b687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53290.89255945])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[2,9,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f15ab2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "53290.89255945"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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