Akshay__Singh__/Day_14/Random_Forest_Algorithm(bug_free)_no file_name_error.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Random Forest for small data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's do it for Regression first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Definition\n",
    "The problem here is to predict the gas consumption (in millions of gallons) in 48 of the US states based on petrol tax (in cents), per capita income (dollars)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('petrol_consumption.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.5</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)  \\\n",
       "0         9.0            3571            1976                         0.525   \n",
       "1         9.0            4092            1250                         0.572   \n",
       "2         9.0            3865            1586                         0.580   \n",
       "3         7.5            4870            2351                         0.529   \n",
       "4         8.0            4399             431                         0.544   \n",
       "\n",
       "   Petrol_Consumption  \n",
       "0                 541  \n",
       "1                 524  \n",
       "2                 561  \n",
       "3                 414  \n",
       "4                 410  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data For Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 0:4].values  \n",
    "y = dataset.iloc[:, 4].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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, random_state=0)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling\n",
    "##We know our dataset is not yet a scaled value, for instance the Average_Income field has values in the range of thousands while Petrol_tax has values in range of tens. Therefore, it would be beneficial to scale our data (although, as mentioned earlier, this step isn't as important for the random forests algorithm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()  \n",
    "X_train = sc.fit_transform(X_train)  \n",
    "X_test = sc.transform(X_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Training the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=20, random_state=0)  \n",
    "regressor.fit(X_train, y_train)  \n",
    "y_pred = regressor.predict(X_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of  our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 51.76500000000001\n",
      "Mean Squared Error: 4216.166749999999\n",
      "Root Mean Squared Error: 64.93201637097064\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now look at  Random Forest for Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Definition\n",
    "The task here is to predict whether a bank currency note is authentic or not based on four attributes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1 = pd.read_csv(\"bill_authentication.csv\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Curtosis</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.6661</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.1674</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.6383</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.5228</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.4552</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variance  Skewness  Curtosis  Entropy  Class\n",
       "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
       "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
       "2   3.86600   -2.6383    1.9242  0.10645      0\n",
       "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
       "4   0.32924   -4.4552    4.5718 -0.98880      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.head()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data For Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset1.iloc[:, 0:4].values  \n",
    "y = dataset1.iloc[:, 4].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "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, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()  \n",
    "X_train = sc.fit_transform(X_train)  \n",
    "X_test = sc.transform(X_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import  RandomForestClassifier\n",
    "\n",
    "classifier = RandomForestClassifier(n_estimators=20, random_state=0)  \n",
    "classifier.fit(X_train, y_train)  \n",
    "y_pred = classifier.predict(X_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now the time to Evaluate our Model (Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[155   2]\n",
      " [  1 117]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       157\n",
      "           1       0.98      0.99      0.99       118\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       275\n",
      "   macro avg       0.99      0.99      0.99       275\n",
      "weighted avg       0.99      0.99      0.99       275\n",
      "\n",
      "0.9890909090909091\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "print(confusion_matrix(y_test,y_pred))  \n",
    "print(classification_report(y_test,y_pred))  \n",
    "print(accuracy_score(y_test, y_pred))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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