scikit_learn_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Scikit-Learn (sklearn)\n",
    "\n",
    "This notebook demostrates some of the most beautiful functions of the beautiful Scikit-Learn library.\n",
    "\n",
    "It consists of:\n",
    "\n",
    "0. An end-to-end Scikit-Learn workflow\n",
    "1. Getting the data ready\n",
    "2. Choose the right estimator/ algorithm for our problems\n",
    "3. Fit the model/algorithm and use it to make predictions on our data\n",
    "4. Evaluating the model\n",
    "5. Improve a model\n",
    "6. Save and load a trained model\n",
    "7. Putting it all together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. An end-to-end Scikit-Learn workflow    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Essentials\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data\n",
    "heart_disease = pd.read_csv('data/heart-disease.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <th>fbs</th>\n",
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       "      <th>slope</th>\n",
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      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "0        0   0     1       1  \n",
       "1        0   0     2       1  \n",
       "2        2   0     2       1  \n",
       "3        2   0     2       1  \n",
       "4        2   0     2       1  \n",
       "..     ...  ..   ...     ...  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  \n",
       "\n",
       "[303 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.Getting the data ready\n",
    "\n",
    "# Create X (feature matrix)\n",
    "X = heart_disease.drop('target', axis = 1)\n",
    "\n",
    "# Create Y (target variable)\n",
    "y = heart_disease['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to display all the rows and columns as sometimes they are omitted in the notebook: \n",
    "\n",
    "def display_all(df):\n",
    "    with pd.option_context(\"display.max_rows\", 1000, \"display.max_columns\", 1000): \n",
    "        display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Choose the right model and hyperparameters\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators = 100)\n",
    "\n",
    "# Keeping the default hyperparameters\n",
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.Fit the model to the training data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a prediction\n",
    "y_preds = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.Evaluate the model on training data and test data\n",
    "\n",
    "# On training data\n",
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7868852459016393"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On test data\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.73      0.75        26\n",
      "           1       0.81      0.83      0.82        35\n",
      "\n",
      "    accuracy                           0.79        61\n",
      "   macro avg       0.78      0.78      0.78        61\n",
      "weighted avg       0.79      0.79      0.79        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Using some more metrics to evaluate the accuracy of the model\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  7],\n",
       "       [ 6, 29]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7868852459016393"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trying model with 10 estimators\n",
      "Model accuracy = 77.05%\n",
      "\n",
      "Trying model with 20 estimators\n",
      "Model accuracy = 75.41%\n",
      "\n",
      "Trying model with 30 estimators\n",
      "Model accuracy = 80.33%\n",
      "\n",
      "Trying model with 40 estimators\n",
      "Model accuracy = 83.61%\n",
      "\n",
      "Trying model with 50 estimators\n",
      "Model accuracy = 80.33%\n",
      "\n",
      "Trying model with 60 estimators\n",
      "Model accuracy = 75.41%\n",
      "\n",
      "Trying model with 70 estimators\n",
      "Model accuracy = 75.41%\n",
      "\n",
      "Trying model with 80 estimators\n",
      "Model accuracy = 78.69%\n",
      "\n",
      "Trying model with 90 estimators\n",
      "Model accuracy = 80.33%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 5.Improve the model\n",
    "# Trying differnt amount of estimators\n",
    "np.random.seed(42)\n",
    "for i in range(10, 100, 10):\n",
    "    print(f\"Trying model with {i} estimators\")\n",
    "    clf = RandomForestClassifier(n_estimators = i)\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(f\"Model accuracy = {clf.score(X_test, y_test) * 100:.2f}%\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6.Save a model and load it\n",
    "import pickle\n",
    "\n",
    "# saving the model\n",
    "pickle.dump(clf, open(\"random_forest_model_1.pkl\", \"wb\")) # wb: write binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8032786885245902"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the model\n",
    "loaded_model = pickle.load(open(\"random_forest_model_1.pkl\", \"rb\")) # rb : read binary\n",
    "loaded_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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