Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "beb102b1",
   "metadata": {},
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e967cf59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Syed M. Aaliyan\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "C:\\Users\\Syed M. Aaliyan\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35830552",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"height-weight.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e53517ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Height\n",
       "0      45     120\n",
       "1      58     135\n",
       "2      48     123\n",
       "3      60     145\n",
       "4      70     160"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "181fb2c8",
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.931142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>0.931142</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Weight    Height\n",
       "Weight  1.000000  0.931142\n",
       "Height  0.931142  1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding co-relations between variable using scatter plot\n",
    "\n",
    "plt.scatter(df['Weight'], df['Height'])\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Height\")\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00bfdd30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x254cd355610>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn for visualization\n",
    "import seaborn as sns\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d6403ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 45],\n",
       "       [ 58],\n",
       "       [ 48],\n",
       "       [ 60],\n",
       "       [ 70],\n",
       "       [ 78],\n",
       "       [ 80],\n",
       "       [ 90],\n",
       "       [ 95],\n",
       "       [ 78],\n",
       "       [ 82],\n",
       "       [ 95],\n",
       "       [105],\n",
       "       [100],\n",
       "       [ 85],\n",
       "       [ 78],\n",
       "       [ 50],\n",
       "       [ 65],\n",
       "       [ 76],\n",
       "       [ 87],\n",
       "       [ 45],\n",
       "       [ 56],\n",
       "       [ 72]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# independent and dependent features\n",
    "\n",
    "X = df[['Weight']]  #independent feature should always be a DataFrame\n",
    "np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8dc89f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120, 135, 123, 145, 160, 162, 163, 175, 182, 170, 176, 182, 175,\n",
       "       183, 170, 177, 140, 159, 150, 167, 129, 140, 160], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df['Height']    #this feature can be a Series\n",
    "np.array(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78be4f1f",
   "metadata": {},
   "source": [
    "# Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19773838",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0215a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0cc59d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardization\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d95596dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21043706],\n",
       "       [ 0.21043706],\n",
       "       [-1.6552288 ],\n",
       "       [ 1.17153765],\n",
       "       [-0.52452222]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = scaler.transform(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f00c702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply Simple Linear Regression\n",
    "regression = LinearRegression(n_jobs = -1)\n",
    "regression.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d95b309f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coffient or Slope is:  [17.03440872]\n",
      "Intercept is:  157.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Coffient or Slope is: \", regression.coef_)\n",
    "print(\"Intercept is: \", regression.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cb0c3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x254ce955f70>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Training data to plot best fit line\n",
    "plt.scatter(X_train, Y_train)\n",
    "plt.plot(X_train, regression.predict(X_train), c='red')"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}