Linear Regression with Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "attachments": {
    "image.png": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/Salary_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[[\"YearsExperience\"]] \n",
    "y=df[\"Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=Pipeline([(\"scaler\",StandardScaler()),(\"Linear Regression\",LinearRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pipeline([(\"task name\",obj),(\"taskname\",obj)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('Linear Regression', LinearRegression())])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9411949620562126"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40748.96184072, 122699.62295594,  64961.65717022,  63099.14214487,\n",
       "       115249.56285456, 107799.50275317])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred=pipe.predict(xtest)\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.988169515729126"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(xtest,ytest)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
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