stage x - comparing Regression techniques -Small Dataset-NO Feature Engineering-diabetes,csv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression - Small Dataset - NO Feature Engineering: diabetes.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(rc={'figure.figsize': [10, 10]}, font_scale=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/Nada Abbas/Desktop/Folder for all/Epsilon AI Diploma/dastasets/diabetes.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x75cd3f2c48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Pregnancies'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x75d33d9f08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Y = df['Glucose']\n",
    "X = df.drop(['Glucose'], axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size= 0.25, random_state=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Gradiend Descend: Logistic Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.3500144988594712\n",
      "Test Score: 0.3184635275199229\n",
      "Root Mean Square Error: 26.151423068323158\n",
      "coefficients: [-0.416354    0.0528283  -0.26766863  0.09291421  0.35026154  2.21755334\n",
      "  0.45248332 24.93124127]\n",
      "b (interception): 80.71414989451662\n"
     ]
    },
    {
     "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>Coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>-0.416354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.052828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.267669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>0.092914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.350262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>2.217553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.452483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>24.931241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Coeff\n",
       "Pregnancies               -0.416354\n",
       "BloodPressure              0.052828\n",
       "SkinThickness             -0.267669\n",
       "Insulin                    0.092914\n",
       "BMI                        0.350262\n",
       "DiabetesPedigreeFunction   2.217553\n",
       "Age                        0.452483\n",
       "Outcome                   24.931241"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "linReg = LinearRegression()\n",
    "linReg.fit(x_train, y_train)\n",
    "y_pred = linReg.predict(x_test)\n",
    "\n",
    "print(f'Training Score: {linReg.score(x_train, y_train)}')\n",
    "print(f'Test Score: {linReg.score(x_test, y_test)}')\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Square Error: {rmse}')\n",
    "\n",
    "print(f'coefficients: {linReg.coef_}')\n",
    "print(f'b (interception): {linReg.intercept_}')\n",
    "pd.DataFrame(linReg.coef_, X.columns, columns=[\"Coeff\"])  # highest score - lower RMSE than SVM-linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- Gradient Descent: Polynomial Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.557395374345681\n",
      "Test Score: -0.12434809986544204\n",
      "Root Mean Square Error: 33.589316818857554\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "x_train_poly = poly.fit_transform(x_train)\n",
    "x_test_poly = poly.fit_transform(x_test)\n",
    "\n",
    "linReg.fit(x_train_poly, y_train)\n",
    "y_pred = linReg.predict(x_test_poly)\n",
    "\n",
    "print(f'Training Score: {linReg.score(x_train_poly, y_train)}')\n",
    "print(f'Test Score: {linReg.score(x_test_poly, y_test)}')\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Square Error: {rmse}')  # hoa ezay l score ykon bel -ve asln ??\n",
    "                                          # Lower value of error than linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- Gradient Descent: SVM - Linear: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.32994381934093\n",
      "Test Score: 0.29175569856861194\n",
      "Root Mean Square Error: 26.658905795815212\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm = SVR(kernel='linear') # select linear kernel since the distribution of datapoints is linear\n",
    "svm.fit(x_train, y_train)\n",
    "y_pred = svm.predict(x_test)\n",
    "\n",
    "print(f'Training Score: {svm.score(x_train, y_train)}')\n",
    "print(f'Test Score: {svm.score(x_test, y_test)}') \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Square Error: {rmse}')  # the best output relating to the RMSE still the scores are very low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4- Gradient Descent: SVM - Polynomial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.036068452789494776\n",
      "Test Score: 0.07309056424162597\n",
      "Root Mean Square Error: 19.252358727954018\n"
     ]
    }
   ],
   "source": [
    "svm = SVR(kernel='poly') \n",
    "svm.fit(x_train, y_train)\n",
    "y_pred = svm.predict(x_test) # very slow model\n",
    "\n",
    "print(f'Training Score: {svm.score(x_train, y_train)}')\n",
    "print(f'Test Score: {svm.score(x_test, y_test)}') \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Square Error: {rmse}')  # lower RMSE and lower accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5- Decision Tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 1.0\n",
      "Test Score: -0.5720760530017617\n",
      "Root Mean Square Error: 39.71801648286748\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "DecTree = DecisionTreeRegressor()\n",
    "DecTree.fit(x_train, y_train)\n",
    "y_pred = DecTree.predict(x_test)\n",
    "\n",
    "print(f'Training Score: {DecTree.score(x_train, y_train)}')\n",
    "print(f'Test Score: {DecTree.score(x_test, y_test)}') \n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f'Root Mean Square Error: {rmse}') # a great example of over fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Linear Regression in this case"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}