Day 2 Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brief Understanding About SImple Linear Regression\n",
    "![image1.PNG](https://image.ibb.co/d7xVdz/1_xc5_CSm_K9d8oe_KYx_Kxen_EGg.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the concept of simple linear regression\n",
    "In simple words linear regression is predicting the value of a variable Y(dependent variable) based on some variable X (independent variable) provided there is a linear relationship between X and Y.\n",
    "<br><br>\n",
    "This linear relationship between the 2 variables can be represented by a straight line (called **regression line**)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/kgFu5e/Day_2_image1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to determine if there is a linear relationship between 2 variables we can simply plot a scatter plot of variable Y with variable X .If the plotted points are randomly scattered that it can be inferred that the variables are not related."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/deBqdz/image2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/jUznQe/image3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When regression line is drawn some points will lie on the regression line other points will lie in the close vicinity of it. This is because our regression line is a **probabilistic model** and our prediction is approximate. So there will be some errors/deviations from actual/observed value of variable Y.\n",
    "![image1.PNG](https://image.ibb.co/hBskBK/image4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But when the linear relationship exist between X and Y we can plot more than one line through these points. Now how do we know which one is the **best fit**?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/b1Gqdz/image5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To help us choose the best line we use the concept of “least squares”.\n",
    "<br>\n",
    "**Least Squares**<br>\n",
    "Y=b0 + b1X + e <br>\n",
    "<br>\n",
    "This the mathematical representation for the regression line where\n",
    "<br><br>\n",
    "Y-Dependant variable.\n",
    "<br><br>\n",
    "X-Independent variable.\n",
    "<br><br>\n",
    "b0 –intercept of the regression line.\n",
    "<br><br>\n",
    "b1-slope of the regression line.\n",
    "<br><br>\n",
    "e- error/deviation from actual/observed value of variable Y.\n",
    "<br><br>\n",
    "Suppose we fit n points of the form (x1,y1) ,(x2,y2)…..(xn,yn)to the above regression line then\n",
    "<br><br>\n",
    "![image1.PNG](https://image.ibb.co/gipMke/equation1.png)\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "Where ei is the difference between ith observed response value and the ith response value that is predicted by our regression line\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our aim here is to minimize this error so that we can get the best possible regression line.\n",
    "\n",
    "Now this error ei can be positive or negative but we are only interested in the magnitude of the error and not in its sign. Hence we square the errors and minimize the sum of **squared errors(SSE)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/bMFu5e/equation2.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/esnJWK/image6.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(In the above graph the green line is the best fit.)\n",
    "\n",
    "## How do we minimize the sum of squared errors(SSE)?\n",
    "Remember that b1 and b0 are still unknown to us.\n",
    "\n",
    "In the least square approach we minimize sum of squared errors(SSE) by choosing the value of b1 and b0 to be (not diving into math of it)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image1.PNG](https://image.ibb.co/nNpAdz/image7.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lets practice some code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing important libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the training and test set\n",
    "training=pd.read_csv('train.csv')\n",
    "testing=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      x          y\n",
       "0  24.0  21.549452\n",
       "1  50.0  47.464463\n",
       "2  15.0  17.218656\n",
       "3  38.0  36.586398\n",
       "4  87.0  87.288984"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    x          y\n",
       "0  77  79.775152\n",
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       "3  20  17.857388\n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training.drop(training.index[[213]], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing thhe Imputer class used to impute the missing values\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer1=Imputer(missing_values='NaN', strategy='mean',axis=0)\n",
    "imputer1=imputer1.fit(training)\n",
    "training=imputer1.transform(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test and Split\n",
    "x_train=training[:,:-1]\n",
    "x_test=testing.iloc[:,:-1].values\n",
    "y_train=training[:,1]\n",
    "y_test=testing.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training the model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLotting the training set\n",
    "plt.scatter(x_train,y_train, color='red')\n",
    "plt.plot(x_train,regressor.predict(x_train),color='blue')\n",
    "plt.title('(Trainig set)')\n",
    "plt.xlabel('X_Plane')\n",
    "plt.ylabel('Y_Plane')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the test set\n",
    "\n",
    "plt.scatter(x_test,y_test, color='red')\n",
    "plt.plot(x_train,regressor.predict(x_train),color='blue')\n",
    "plt.title('(Test set)')\n",
    "plt.xlabel('X_Plane')\n",
    "plt.ylabel('Y_Plane')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}