Linear Regression .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef7d3edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "187f10de",
   "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>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.767874</td>\n",
       "      <td>59.627484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.107671</td>\n",
       "      <td>36.146453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.266917</td>\n",
       "      <td>46.912878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.815180</td>\n",
       "      <td>29.125660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.753758</td>\n",
       "      <td>68.170414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age     Height\n",
       "0  14.767874  59.627484\n",
       "1   3.107671  36.146453\n",
       "2   7.266917  46.912878\n",
       "3   1.815180  29.125660\n",
       "4  16.753758  68.170414"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_pickle(r'C:\\Users\\HP\\Downloads\\AgesAndHeights.pkl')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4327601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Age'}>,\n",
       "        <AxesSubplot:title={'center':'Height'}>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132c4a92",
   "metadata": {},
   "source": [
    "We can see in the Age histogram that some age values are below 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81e11425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['Age']>0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe2eb966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Height in Inches')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Age'],df['Height'])\n",
    "plt.title('Data after removing impossible Age values')\n",
    "plt.xlabel('Age in Years')\n",
    "plt.ylabel('Height in Inches')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "095dd087",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['Age'],df['Height'],test_size=0.3,random_state=23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ee772f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()\n",
    "X_train=np.array(X_train)\n",
    "model.fit(X_train.reshape(-1,1),y_train)\n",
    "X_test=np.array(X_test)\n",
    "y_pred=model.predict(X_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "160943ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d70e912460>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test,color=\"Green\")\n",
    "plt.scatter(X_test,y_pred,color=\"Red\")\n",
    "plt.xlabel('Age in Years')\n",
    "plt.ylabel('Height in Inches')\n",
    "plt.legend(labels = ('Actual','Predicted'),loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed9b5101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Square Error(MSE) or J(theta) is:  6.8487848589907765\n",
      "R square obtain for scikit learn library is : 0.9415279458010771\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "R_square = model.score(X_test.reshape(-1,1),y_test)\n",
    "\n",
    "print('The Mean Square Error(MSE) or J(theta) is: ',mse)\n",
    "print('R square obtain for scikit learn library is :',R_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b78b3a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}