Heart disease prediction using Machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Definition\n",
    "\n",
    "Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "~The problem we will be exploring is binary classification (a sample can only be one of two things).\n",
    "\n",
    "## 2.Data\n",
    "\n",
    "The data is from the Cleveland database from UCI Machine Learning Repository.\n",
    "\n",
    "## 3.Features\n",
    "\n",
    "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl \n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        - can range from mild symptoms to severe problems\n",
    "        - signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        - Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved \n",
    "9. exang - exercise induced angina (1 = yes; 0 = no) \n",
    "10. oldpeak - ST depression induced by exercise relative to rest \n",
    "    * looks at stress of heart during excercise\n",
    "    * unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy \n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising \n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np # np is short for numpy\n",
    "import pandas as pd # pandas is so commonly used, it's shortened to pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # seaborn gets shortened to sns\n",
    "from warnings import simplefilter\n",
    "# ignore all future warnings\n",
    "simplefilter(action='ignore', category = FutureWarning)\n",
    "\n",
    "# We want our plots to appear in the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "## Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "## Model evaluators\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading data\n",
    "df = pd.read_csv(\"heart-disease (2).csv\") # 'DataFrame' shortened to 'df'\n",
    "df.shape # (rows, columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "Comparing different columns to each other or with target valriable, so that we become more and more familiar with the dataset. we can also draw some insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the top 5 rows of our dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how many positive (1) and negative (0) samples we have in our dataframe\n",
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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tI0kNMtwlqUGGuyQ1yHCXpAYZ7pLUoP8B4304fv57Ts0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the value counts with a bar graph\n",
    "df.target.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two values are close to each other hence we can consider this as Balanced dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency according to Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\", figsize=(10,6), color=[\"pink\", \"lightblue\"])\n",
    "\n",
    "# Add some attributes to it\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0); # keep the labels on the x-axis vertical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age vs Max Heart rate for Heart Disease\n",
    "\n",
    "Let's try combining a couple of independent variables, such as, age and thalach (maximum heart rate) and then comparing them to our target variable heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Start with positve examples\n",
    "plt.scatter(df.age[df.target==1], \n",
    "            df.thalach[df.target==1], \n",
    "            c=\"green\") # define it as a scatter figure\n",
    "\n",
    "# Now for negative examples, we want them on the same plot, so we call plt again\n",
    "plt.scatter(df.age[df.target==0], \n",
    "            df.thalach[df.target==0], \n",
    "            c=\"yellow\") # axis always come as (x, y)\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.legend([\"Disease\", \"No Disease\"])\n",
    "plt.ylabel(\"Max Heart Rate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems the younger someone is, the higher their max heart rate (dots are higher on the left of the graph) and the older someone is, the more green dots there are. But this may be because there are more dots all together on the right side of the graph (older participants)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the age **distribution**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "df.age.plot.hist(color = 'violet');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency per Chest Pain Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new crosstab and base plot\n",
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\", \n",
    "                                   figsize=(10,6), \n",
    "                                   color=[\"lightblue\", \"salmon\"])\n",
    "\n",
    "# Add attributes to the plot to make it more readable\n",
    "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain Type\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "    \n",
    "It's interesting the atypical agina (value 1) states it's not related to the heart but seems to have a higher ratio of participants with heart disease than not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation between independent variables\n",
    "\n",
    "We'll compare all of the independent variables in one hit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.0, 0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(15, 10))\n",
    "ax = sns.heatmap(corr_matrix, \n",
    "            annot=True, \n",
    "            linewidths=0.5, \n",
    "            fmt= \".2f\", \n",
    "            cmap=\"YlGnBu\");\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top -0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A higher positive value means a potential positive correlation (increase) and a higher negative value means a potential negative correlation (decrease).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## modeling\n",
    "\n",
    "We're trying to predict our target variable using all of the other variables.\n",
    "\n",
    "To do this, we'll split the target variable from the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Everything except target variable\n",
    "X = df.drop(\"target\", axis=1)\n",
    "\n",
    "# Target variable\n",
    "y = df.target.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and test split\n",
    "\n",
    "Here I have splitted the data as 80% as train data and 20% test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Split into train & test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size = 0.2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model choices\n",
    "\n",
    "Now we've got our data prepared, we can start to fit models. We'll be using the following and comparing their results.\n",
    "\n",
    "1. Logistic Regression - [`LogisticRegression()`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
    "2. K-Nearest Neighbors - [`KNeighboursClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
    "3. RandomForest - [`RandomForestClassifier()`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the algorithms in the Scikit-Learn library use the same functions, for training a model, model.fit(X_train, y_train) and for scoring a model model.score(X_test, y_test). \n",
    "\n",
    "Since the algorithms we've chosen implement the same methods for fitting them to the data as well as evaluating them, let's put them in a dictionary and create a which fits and scores them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"KNN\": KNeighborsClassifier(),\n",
    "          \"Logistic Regression\": LogisticRegression(), \n",
    "          \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    # Random seed for reproducible results\n",
    "    np.random.seed(42)\n",
    "    # Make a list to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN': 0.6885245901639344,\n",
       " 'Logistic Regression': 0.8688524590163934,\n",
       " 'Random Forest': 0.8524590163934426}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores = fit_and_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison\n",
    "Since we've saved our models scores to a dictionary, we can plot them by first converting them to a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pyVzqTZKfVNXu460bBL1cWKRxJHnXejZXVb1no4XRI/E54E3AlcADDWdRj5IcBLwA2D7Jx0ds2hJY00yqZlnok+NPY6zbDDiWzhQIFvrUtqqqvt10CE3YLcBy4BA6f4yH3U3nD/TAcchlkiXZAngDnRkozwNOr6pbm02l9UlyGp1rLP4NuG94fVX9pLFQ6lmSGVW1uvt4a2CHqvp5w7Ea4RH6JEmyDXAi8HI6M0/uXlV3NptKPRqebG7k/B8FPLeBLJq4i5McQqfPrgJWJvl+VZ3YcK6NzkKfBEk+DBxG5wq1Xavqjw1H0gRU1X5NZ9CjslVV3ZXkWOCsqvqHJAN5hO5pi5PjzcAc4GTgliR3dT/uTnJXw9k0jiRbJflIkuXdj9OTbNV0LvVsepInAEcA32w6TJMs9ElQVZtU1V9U1RZVteWIjy2qasum82lcZ9J5I+2I7sddwFmNJtJEnEpnrqn/rKorkjwJuK7hTI3wTVENvCRXVdUzxlsnTXUeoU+C4aGV7ue7Ryzfk2Qgz4ftM39O8pzhhe6FRn9uMI8mIMnOSb6b5Bfd5QVJTm46VxM8Qt8Auqcuvg74W+CCqnpzw5G0HkmeQefMpK3oXCV6B3B0Vf2s0WDqSZLvA28FPlNVu3XX/aKqnt5sso3Ps1wmUZLHAm8EXgWcAzyzqm5vNpXGU1VXAQuTbNld9o3s/rJZVf141H3pB/KVsYU+CZJsR+dMl5fReYNtt6pa1WwqjSfJK6rq7CQnjloPQFV9pJFgmqjbkjyZ7n2Mk7wU+H2zkZphoU+O3wAr6ZwZcQ9wzMijBYthyprV/bxFoyn0aB1P5xqQ/57kd8CNdC7wGziOoU+CJO+me3QwhqqqUzdiHGlgJNkEeGlVnZdkFrBJVd3ddK6mWOiTIMncqhpax7aDq+obGzuTepfkQ8B76ZzZ8h1gIfDGqjq70WDqSZJLq2qfpnNMBZ62ODm+m2Te6JVJFgMf3ehpNFEHdN8IfREwBOxM56wJ9YeLk7wlyQ5Jthn+aDpUExxDnxxvovOf6gVVdR1AkncA/xP460aTqRczup9fAHylqu4YdcaEpra/6X4+fsS6AgbuBiUW+iSoqguT3Ad8O8mL6cyD/kxgH2dc7AvfSPIrOkMur0syG7i34UzqUVXNbzrDVOEY+iTqXm34NeCHwBFVZSn0ie482ndV1QNJNgO2rKr/23QujS/JDODvgOFx9O/RuchodWOhGmKhT4Ikd9N5iRfgMcBqOrcyG743pRN0TWHdGwp/p6ru7l4yvjvwXm9w0R+SfJbOsNkXuqteCTxQVcc2l6oZFroGXpKfV9WC7iusDwD/CLyzqp49zpdqCkjys6paON66QeBZLtJDN4Z+IfDPVfV1YNMG82hiHuheKQpAd/rcgbzZt2+KSvC7JJ8Bngd8MMlj8GCnn7wVuCTJDXSGOZ8ILG42UjMcctHA674JeiBwdVVd1737za5V9e8NR1OPun+En0Kn0H9VVfeN8yWt5FGIBl5V3QPcCgzPib6GAb3jTT9J8v4Ri/tU1c+r6meDWubgEbpEkn8AFgFPqaqdk8wB/rWq9m44mtYjyU+qavfRjweZR+gSvAQ4BPgTQFXdgjMwqg/5pqgE91dVJRmeT3vWeF+gKeFx3bnsM+LxWoM4bbWFLsF53bNcHpvkODpzg/xLw5k0vn/hoVdSIx8PLMfQJSDJ84ED6BztXVRVFzccSZowC10DLck0OgX+vKazSI+Wb4pqoFXVA8A9SbZqOov0aDmGLnWmyr06ycV0z3QBqKrXNxdJmjgLXYJvdT/Uh5I8FngVMI8RnTaIf5AdQ5fU15L8ELgcuBp4cHh9VX1hnV/UUha6Bl6Sq+nMZz/SKmA5nXnRb9/4qdQrrxJ9iIWugZfkQ3SmWz2nu+pIOqcvrgKeU1UHN5VN40vyJuCPwDeBtfO4VNUdjYVqiIWugZfkB6PnbRlel+Tqqtq1qWwaX5LjgfcBf+ChV1pVVd4kWhpAmyd5dlX9CCDJs4DNu9vWNBdLPToR+Muquq3pIE2z0CU4FjgzyXCJ3w0c253T5QPNxVKPrgHuaTrEVOCQi9TVvbgoVfWHprOod0kuAJ4GXMJ/HUMfuNMWPULXwEvyeOD9wJyqOijJLsBfVdXnGo6m3nyt+zHwPELXwEvybeAs4O+ramGS6cBPfTO0fyTZFNi5u7iiqlY3macpzuUiwXZVdR7di1Kqag0Detf4fpRkXzq3DDwD+BTw6yT7NBqqIQ65SPCnJNvSPeUtyZ50zkFXfzgdOKCqVgAk2Rn4CrBHo6kaYKFLndPelgJPTvIDYDZweLORNAEzhsscoKp+nWRGk4Ga4hi6BHTHzZ9C5wrRgR2D7UdJzqTz6upL3VUvB6ZX1eLmUjXDQpdG6d696G1V9fyms2h8SR4DHA88h84f5EuBT1XVfev9whay0DWwkjwX+DQwh85pb+8HvkinFN5XVf/WYDxpwix0DawkPwXeBFwGHESnzE+pqo81Gkw9WccsmWtV1YKNGGdKsNA1sEZPu5rkP6vqyU1mUu+SPLH78Pju55Fj6PdU1akbP1WzLHQNrCQ3AG8ZseofRy475NIf1jdbZlOZmuJpixpk3wcOXsdyARZ6f5iV5DlV9R8ASfYCZjWcqREeoUvqa0n2AM4Etuqu+gPwN1X1k+ZSNcNCl9QKSbak02kDe5WvhS6pr3XPQ/8fwDxGDCMP4puijqFL6ndfpzP3zpWMmA99EHmEroHXvSfll4dvbJFka+CoqvpUs8nUiyS/qKqnN51jKnD6XAmOG3mXoqq6EziuwTyamB8mce56HHKRADZJkuq+XE0yDdi04Uzq3XOAo5PcSGfIJUAN4pWiFroEFwHnJfk0nfPPXwt8p9lImoCDmg4wVTiGroGXZBPgb4H96Rzd/Tvw2aryrkV9JMnjgJnDy1X12wbjNMJCl9TXkhxC565Fc4BbgScC11bV0xoN1gCHXDSwkpxXVUesa9a+QRyD7VPvAfYEllXVbkn2A45qOFMjLHQNsjd0P7+o0RR6tFZX1e1JNkmySVVdkuSDTYdqgqctamBV1e+7D19XVb8Z+QG8rslsmpA/JNmczp2KvpzkY8CahjM1wjF0DbzR86J31/3cIZf+kGQW8Gc6B6gvpzNJ15er6vZGgzXAQtfASvJ3dI7EnwxcP2LTFsAPquoVjQTTo9K9juDIqvpy01k2NgtdAyvJVsDWwAeAk0Zsuruq7mgmlXrVnV3xeGB7YClwcXf5rcBVVXVog/EaYaFr4CV5MjBUVfcl2RdYAHxx5HQAmnqSfB24k849Yfen88d5U+ANVXVVk9maYqFr4CW5ClhEZ/rVi+gc7T2lql7QZC6tX5Krq2rX7uNpwG3AjlV1d7PJmuNZLhI8WFVrgMOAj1bVm4AnNJxJ41s9/KB7Ve+Ng1zm4HnoEsDqJEcBr+Khe4rOaDCPerMwyV3dxwH+ors8PDnXls1Fa4aFLsFiOhNyva+qbkwyHzi74UwaR1VNazrDVOMYuiS1hEfoGljO5aK28QhdAyvJE6rq90meONb27hQAUt+w0CWpJRxy0cBLcjcPH3JZBSwH3lxVN2z8VNLEWegSfAS4BTiHzilvRwL/DVgBnAns21gyaQIcctHAS/Kjqnr2qHWXV9WeSX5WVQubyiZNhFeKSvBgkiOGb5CQ5IgR2zziUd/wCF0DL8mTgI8Bf9VddRnwJuB3wB5V9R9NZZMmwkKXpJZwyEUDL8ncJBckuTXJ/0tyfpK5TeeSJspCl+AsOlPmzqFzs4RvdNdJfcUhFw28JFdV1TPGWydNdR6hS3Bbklckmdb9eAUwcDcYVv/zCF0DL8mOwCfpnOVSwA+B11fVbxsNJk2QhS6NIckbq+qjTeeQJsJCl8aQ5LdVtWPTOaSJcAxdGluaDiBNlIUujc2Xruo7zraogbWOaXOhe8PhjRxHetQcQ5eklnDIRZJawkKXpJaw0CWpJSx0SWoJC12SWsJCl6SW+P+jGqyrQMgAXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=['accuracy'])\n",
    "model_compare.T.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the LogisticRegression() model performs best."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning and cross-validation\n",
    "\n",
    "Tune KNeighborsClassifier (K-Nearest Neighbors or KNN) by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of train scores\n",
    "train_scores = []\n",
    "\n",
    "# Create a list of test scores\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21) # 1 to 20\n",
    "\n",
    "# Setup algorithm\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different neighbors values\n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors = i) # set neighbors value\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Update the training scores\n",
    "    train_scores.append(knn.score(X_train, y_train))\n",
    "    \n",
    "    # Update the test scores\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum KNN score on the test data: 75.41%\n"
     ]
    },
    {
     "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.plot(neighbors, train_scores, label=\"Train score\")\n",
    "plt.plot(neighbors, test_scores, label=\"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model score\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph, `n_neighbors = 11` seems best.\n",
    "\n",
    "Even knowing this, the `KNN`'s model performance didn't get near what `LogisticRegression` or the `RandomForestClassifier` did.\n",
    "\n",
    "Because of this, we'll discard `KNN` and focus on the other two.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning models with with RandomizedSearchCV\n",
    "RandomizedSearchCV - tries n_iter combinations of hyperparameters and saves the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Different RandomForestClassifier hyperparameters\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.8s finished\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for LogisticRegression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv=5,\n",
    "                                n_iter=20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model\n",
    "rs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  1.4min finished\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomForestClassifier\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv=5,\n",
    "                           n_iter=20,\n",
    "                           verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search model\n",
    "rs_rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 560,\n",
       " 'min_samples_split': 12,\n",
       " 'min_samples_leaf': 15,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning a model with GridSearchCV\n",
    "\n",
    "GridSearchCV - tries every single combination of hyperparameters and saves the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.7s finished\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# Different LogisticRegression hyperparameters\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search model\n",
    "gs_log_reg.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating a classification model, beyond accuracy\n",
    "\n",
    "Confusion matrix, Classification report, Precision,  Recall, F1-score\n",
    "\n",
    "Scikit-Learn has these all built-in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make preidctions on test data\n",
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix\n",
    "\n",
    "A confusion matrix is a visual way to show where your model made the right predictions and where it made the wrong predictions (or in other words, got confused)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Display confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15.5, -1.5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5) # Increase font size\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "  \n",
    "    fig, ax = plt.subplots(figsize=(2, 2))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "    plt.xlabel(\"true label\")\n",
    "    plt.ylabel(\"predicted label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top -0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the model gets confused (predicts the wrong label) relatively the same across both classes. In essence, there are 4 occasaions where the model predicted 0 when it should've been 1 (false negative) and 3 occasions where the model predicted 1 instead of 0 (false positive).\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Classification report\n",
    "\n",
    "We can make a classification report using [`classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) and passing it the true labels as well as our models predicted labels. \n",
    "\n",
    "A classification report will also give us information of the precision and recall of our model for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show classification report\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we've got a few deeper insights on our model. But these were all calculated using a single training and test set.\n",
    "\n",
    "What we'll do to make them more solid is calculate them using cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cross_val_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Instantiate best model with best hyperparameters (found with GridSearchCV)\n",
    "clf = LogisticRegression(C=0.23357214690901212,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-validated Accuracy score\n",
    "cv_accuracy = np.mean(cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5, # 5-fold cross-validation\n",
    "                         scoring=\"accuracy\")) # accuracy as scoring\n",
    "\n",
    "\n",
    "\n",
    "# Cross-validated precision score\n",
    "cv_precision = np.mean(cross_val_score(clf,\n",
    "                                       X,\n",
    "                                       y,\n",
    "                                       cv=5, # 5-fold cross-validation\n",
    "                                       scoring=\"precision\")) # precision as scoring\n",
    "\n",
    "# Cross-validated recall score\n",
    "cv_recall = np.mean(cross_val_score(clf,\n",
    "                                    X,\n",
    "                                    y,\n",
    "                                    cv=5, # 5-fold cross-validation\n",
    "                                    scoring=\"recall\")) # recall as scoring\n",
    "\n",
    "\n",
    "# Cross-validated F1 score\n",
    "cv_f1 = np.mean(cross_val_score(clf,\n",
    "                                X,\n",
    "                                y,\n",
    "                                cv=5, # 5-fold cross-validation\n",
    "                                scoring=\"f1\")) # f1 as scoring\n",
    "\n",
    "\n",
    "# Visualizing cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_accuracy,\n",
    "                            \"Precision\": cv_precision,\n",
    "                            \"Recall\": cv_recall,\n",
    "                            \"F1\": cv_f1},\n",
    "                          index=[0])\n",
    "cv_metrics.T.plot.bar(title=\"Cross-Validated Metrics\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final thing to check off the list of our model evaluation techniques is feature importance.\n",
    "\n",
    "## Feature importance\n",
    "\n",
    "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model?\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00369921, -0.90424048,  0.67472836, -0.01161342, -0.00170365,\n",
       "         0.04787695,  0.33490174,  0.02472939, -0.63120364, -0.57590734,\n",
       "         0.47095031, -0.6516537 , -0.69984162]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of LogisticRegression \n",
    "clf.fit(X_train, y_train);\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Match features to columns\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_df = pd.DataFrame(features_dict, index=[0])\n",
    "features_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice some are negative and some are positive.\n",
    "\n",
    "The larger the value (bigger bar), the more the feature contributes to the models decision.\n",
    "\n",
    "If the value is negative, it means there's a negative correlation. And vice versa for positive values.\n",
    "\n",
    "Since Logistic Regression scored the high value, we can consider using Logistic regression.\n",
    "\n",
    "We may not reach the 95 - 98% accuracy with the models, However machine learning techniques can be used to predict the output from existing data.\n"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}