Day2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression\n",
    "\n",
    "### <font color=Crimson >  Predicting a response using a single feature. </font>\n",
    "\n",
    "It is a method to predict dependent variable (Y) based on values of independent variables (X). It is assumed that the two variables are linearly related. Hence, we try to find a linear function that predicts the reponse value (y) as accurately as possible as a function of the feature or independent vaiable (x).\n",
    "\n",
    "### <font color=Crimson > How to find the best fit line? </font>\n",
    "\n",
    "In this regression model, we are trying to minimize the errors in prediction by finding the \"line of best fit\" - the regression line from the errors would be minimal. We are trying to minimize the length between the observed value (Yi) and the predicted value from our model (Yp).\n",
    "\n",
    "In this regression task, we will predict the precentage of marks that a student is expected to score based upon the number of hours they sutdied.\n",
    "\n",
    "### <font color=Crimson >  使用單一特徵來預測 </font>\n",
    "\n",
    "這是一種基於自變量值 (X) 來預測應變量值 (Y) 的方法。假設這兩個變量是具有線性相關的。因此，試著尋找一種根據特徵或自變量 (X) 的線性函數來精確預測應變值 (Y)\n",
    "\n",
    "### <font color=Crimson > 如何找到最佳的擬合線? </font>\n",
    "\n",
    "在迴歸模型中，透過找到\"最佳的擬合線\"來最小化預測誤差 - 回歸線的誤差將會是最小的。試著去最小化觀測值 (Yi) 和模型預測值(Yp) 之間的長度。\n",
    "\n",
    "在這個迴歸工作中，將根據學生學習的小時數來預測得分的分數百分比。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will follow the same steps as in my previous infographic of Data Preprocessing.\n",
    "\n",
    "* Import the Libraries.  \n",
    "* Import the DataSet.  \n",
    "* Check for Missing Data.  \n",
    "* Split the DataSet.  \n",
    "* Feature Scaling will be taken care by the Library we will use for Simple Linear Regression Model.  \n",
    "\n",
    "將按照 Day 1 的資料預處理方式進行相同步驟\n",
    "\n",
    "* 載入相關套件\n",
    "* 載入資料集\n",
    "* 檢查遺失資料\n",
    "* 分割資料集\n",
    "* 特徵縮放將使用簡單線性迴歸模型來進行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./datasets/studentscores.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5],\n",
       "       [5.1],\n",
       "       [3.2],\n",
       "       [8.5],\n",
       "       [3.5]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:, :1].values\n",
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dataset.iloc[:, 1].values\n",
    "Y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.8],\n",
       "       [6.9],\n",
       "       [1.1],\n",
       "       [5.1],\n",
       "       [7.7]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 27, 69, 30, 62])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Fitting Simple Linear Regression Model to the training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fit the dataset into the model we will use LinearRegression class from sklearn.linear_model library. Then we make an object regressor of LinearRerssion Class. Now we will fit the regressor object into our dataset using fit() method of LinearRegression Class.\n",
    "\n",
    "將使用 sklearn.linear_model 中的 LinearRegression，作為使用模型來訓練資料集。會建立一個 LinearRegression 的 regressor 物件。利用使用 LinearRegression中的 fit()，將 regressor 物件對資料集進行訓練。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor = regressor.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Predecting the Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will predict the observations from our test set. We will save the output in a vector Y_pred. To predict the result we use predict method of LinearRegression Class on the regressor we trained in the previous step.\n",
    "\n",
    "透過測試集來預測的結果。將輸出保存在向量 Y_pred中。使用先前訓練的迴歸模型 regressor ，利用 LinearRegression 的預測方法來對進行預測。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.84472176, 33.74557494, 75.50062397, 26.7864001 , 60.58810646,\n",
       "       39.71058194, 20.8213931 ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step is to visualize our results. We will use matplotlib.pyplot library to make Scatter Plots of our Training set results and Test set results to see how close our model predicted the Values.\n",
    "\n",
    "最後一步是將結果可視化。使用 matplotlib.pyplot 對訓練結果和測試結果做散佈圖，來檢視模型的預測效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Training Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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_train, Y_train, color = 'red')\n",
    "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Test Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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 = 'red')\n",
    "plt.plot(X_test, regressor.predict(X_test), color = 'blue')\n",
    "#plt.plot(X_test, Y_pred, color = 'blue')    #和前一行相同的結果\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}