Project_Assignment_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import Successful\n"
     ]
    }
   ],
   "source": [
    "# Importing the necessary libraries \n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Import Successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Python_Edureka\\\\projects\\\\Project_3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the working directory to where the dataset resides\n",
    "os.chdir(\"F:\\\\Python_Edureka\\\\projects\\\\Project_3\")\n",
    "\n",
    "# Verify the set working directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Importing train_data file into pandas Dataframe\n",
    "train_data = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# Display the first five rows of the train_data set\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing test_data file into pandas Dataframe\n",
    "test_data = pd.read_csv(\"test.csv\")\n",
    "\n",
    "# Display the first five rows of the test_data set\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>0.003906</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.035156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.022461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020508</td>\n",
       "      <td>0.002930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>84</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.048828</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  species   margin1   margin2   margin3   margin4   margin5   margin6  \\\n",
       "0   1        3  0.007812  0.023438  0.023438  0.003906  0.011719  0.009766   \n",
       "1   2       49  0.005859  0.000000  0.031250  0.015625  0.025391  0.001953   \n",
       "2   3       65  0.005859  0.009766  0.019531  0.007812  0.003906  0.005859   \n",
       "3   5       94  0.000000  0.003906  0.023438  0.005859  0.021484  0.019531   \n",
       "4   6       84  0.005859  0.003906  0.048828  0.009766  0.013672  0.015625   \n",
       "\n",
       "    margin7  margin8  ...  texture55  texture56  texture57  texture58  \\\n",
       "0  0.027344      0.0  ...   0.007812   0.000000   0.002930   0.002930   \n",
       "1  0.019531      0.0  ...   0.000977   0.000000   0.000000   0.000977   \n",
       "2  0.068359      0.0  ...   0.154300   0.000000   0.005859   0.000977   \n",
       "3  0.023438      0.0  ...   0.000000   0.000977   0.000000   0.000000   \n",
       "4  0.005859      0.0  ...   0.096680   0.000000   0.021484   0.000000   \n",
       "\n",
       "   texture59  texture60  texture61  texture62  texture63  texture64  \n",
       "0   0.035156        0.0        0.0   0.004883   0.000000   0.025391  \n",
       "1   0.023438        0.0        0.0   0.000977   0.039062   0.022461  \n",
       "2   0.007812        0.0        0.0   0.000000   0.020508   0.002930  \n",
       "3   0.020508        0.0        0.0   0.017578   0.000000   0.047852  \n",
       "4   0.000000        0.0        0.0   0.000000   0.000000   0.031250  \n",
       "\n",
       "[5 rows x 194 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label Encoding the categorical variables of the train_data dataset\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "train_data[\"species\"] = le.fit_transform(train_data[\"species\"])\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating X and y out of the train_data\n",
    "X = train_data.iloc[:,2:].values\n",
    "y = train_data.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data using train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applyong Random Forest Classifier for the train_data\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_clf = RandomForestClassifier(n_estimators = 100)\n",
    "rf_model = rf_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Model Accuracy:  0.9595959595959596\n"
     ]
    }
   ],
   "source": [
    "# Predicting the random forest model on the X_test\n",
    "\n",
    "y_rf_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Checking the accuracy of the random forest model\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Random Forest Model Accuracy: \", accuracy_score(y_rf_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a Support Vector Machine Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting a Support Vector Machine for data\n",
    "\n",
    "from sklearn import svm\n",
    "svm_clf = svm.SVC()\n",
    "model_svm = svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy: 0.7828282828282829\n"
     ]
    }
   ],
   "source": [
    "# Predicting the svm model on the  X_test data\n",
    "\n",
    "y_svm_pred = model_svm.predict(X_test)\n",
    "\n",
    "# Checking the accuracy of the svm model\n",
    "\n",
    "print(\"SVM accuracy:\", accuracy_score(y_svm_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a Naive Bayes Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb_model = gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive_Bayes accuracy: 0.48484848484848486\n"
     ]
    }
   ],
   "source": [
    "y_pred_gnb = gnb_model.predict(X_test)\n",
    "print(\"Naive_Bayes accuracy:\", accuracy_score(y_pred_gnb, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "dt = tree.DecisionTreeClassifier()\n",
    "model_dt = dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision_tree accuracy: 0.5606060606060606\n"
     ]
    }
   ],
   "source": [
    "y_pred_dt = model_dt.predict(X_test)\n",
    "print(\"Decision_tree accuracy:\", accuracy_score(y_pred_dt, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The classifier with the best accuracy is Random Forest Classifier Model. We apply this model to the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier Model on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>margin1</th>\n",
       "      <th>margin2</th>\n",
       "      <th>margin3</th>\n",
       "      <th>margin4</th>\n",
       "      <th>margin5</th>\n",
       "      <th>margin6</th>\n",
       "      <th>margin7</th>\n",
       "      <th>margin8</th>\n",
       "      <th>margin9</th>\n",
       "      <th>margin10</th>\n",
       "      <th>...</th>\n",
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       "      <th>texture57</th>\n",
       "      <th>texture58</th>\n",
       "      <th>texture59</th>\n",
       "      <th>texture60</th>\n",
       "      <th>texture61</th>\n",
       "      <th>texture62</th>\n",
       "      <th>texture63</th>\n",
       "      <th>texture64</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.053711</td>\n",
       "      <td>Quercus_Agrifolia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.064453</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033203</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006836</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.037109</td>\n",
       "      <td>0.044922</td>\n",
       "      <td>Quercus_Afares</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.021484</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Acer_Circinatum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.011719</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.036133</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008789</td>\n",
       "      <td>Castanea_Sativa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.010742</td>\n",
       "      <td>0.041016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.009766</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>Alnus_Viridis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    margin1   margin2   margin3   margin4   margin5   margin6   margin7  \\\n",
       "0  0.019531  0.009766  0.078125  0.011719  0.003906  0.015625  0.005859   \n",
       "1  0.007812  0.005859  0.064453  0.009766  0.003906  0.013672  0.007812   \n",
       "2  0.000000  0.000000  0.001953  0.021484  0.041016  0.000000  0.023438   \n",
       "3  0.000000  0.000000  0.009766  0.011719  0.017578  0.000000  0.003906   \n",
       "4  0.001953  0.000000  0.015625  0.009766  0.039062  0.000000  0.009766   \n",
       "\n",
       "   margin8   margin9  margin10  ...  texture56  texture57  texture58  \\\n",
       "0      0.0  0.005859  0.023438  ...   0.000000   0.015625   0.000977   \n",
       "1      0.0  0.033203  0.023438  ...   0.000000   0.006836   0.001953   \n",
       "2      0.0  0.011719  0.005859  ...   0.000000   0.000977   0.000000   \n",
       "3      0.0  0.003906  0.001953  ...   0.015625   0.002930   0.036133   \n",
       "4      0.0  0.005859  0.000000  ...   0.042969   0.016602   0.010742   \n",
       "\n",
       "   texture59  texture60  texture61  texture62  texture63  texture64  \\\n",
       "0   0.015625        0.0        0.0   0.000000   0.003906   0.053711   \n",
       "1   0.013672        0.0        0.0   0.000977   0.037109   0.044922   \n",
       "2   0.000000        0.0        0.0   0.015625   0.000000   0.000000   \n",
       "3   0.013672        0.0        0.0   0.089844   0.000000   0.008789   \n",
       "4   0.041016        0.0        0.0   0.007812   0.009766   0.007812   \n",
       "\n",
       "             species  \n",
       "0  Quercus_Agrifolia  \n",
       "1     Quercus_Afares  \n",
       "2    Acer_Circinatum  \n",
       "3    Castanea_Sativa  \n",
       "4      Alnus_Viridis  \n",
       "\n",
       "[5 rows x 193 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = test_data.iloc[:, 1:]\n",
    "pred_rf = rf_clf.predict(test_data)\n",
    "\n",
    "test_data[\"species\"] = le.inverse_transform(pred_rf)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the predicted data \n",
    "test_data.to_csv(\"Output.csv\", index = True)"
   ]
  }
 ],
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