Splitting the Data to 60-20-20 ratio vs. 80-10-10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the project, we are give a dataset of Iris flowers. It contains some characteristics/features of the flower and which species the flower belongs to. We have to perform Exploratory Data Analysis of each of the features, label encode the flower variety and conduct 2 experiments. \n",
    "\n",
    "In first experiment, we need to split the dataset into 60-20-20 ratio meaning 60% of the data will be used to train the model, 20% will be used to validate the models and 20% will be used to get unbiased result of the best performing model which we get from the validate dataset. We need to perform the KFold Cross Validation to find a baseline performace of the models, perform Grid Search to find optimal hyperparameters for the model which inturn will increase its accuarcy, and finally compare the 3 models which we have selected using classification metrics like Accuracy, Precision, Recall and Latency of the model.\n",
    "\n",
    "In the second experiment, the change will be only on how we split the dataset. Here, we split the dataset into 80-10-10\n",
    "ratio. 80% will go to training the model, 10% will go to validate the model and 10% will go to test the model. Other all the steps are same.\n",
    "\n",
    "Finally, we need to conclude, by explaining which split gave us the best model and explain what might be the reason for that. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Iris dataset consists of features related to iris flowers species. We have 5 columns in the dataset. They are:\n",
    "\n",
    "1. Sepal Length\n",
    "2. Sepal Width\n",
    "3. Petal Length\n",
    "4. Petal Width\n",
    "5. Species\n",
    "\n",
    "The top four columns contains the raw data which might be able to explain which species/class iris flower we are looking at. We have Setosa, Versicolor and Virginica as our classes. The dataset consists of 50 records for each species making the total row count of 150. The dataset is evenly distributed. The setosa class can be linearly separated however, other 2 classes are not linearly separable from each other making the dataset simple but interesting to test our models.  \n",
    "\n",
    "The dataset is best known to be used frequently in the pattern recognition literature. The orginal paper and usage can be found in the UCI Machine Learning Repository -- Iris Data Set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary dependencies\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width variety\n",
       "0           5.1          3.5           1.4          0.2  Setosa\n",
       "1           4.9          3.0           1.4          0.2  Setosa\n",
       "2           4.7          3.2           1.3          0.2  Setosa\n",
       "3           4.6          3.1           1.5          0.2  Setosa\n",
       "4           5.0          3.6           1.4          0.2  Setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the dataset we will be working on\n",
    "iris = pd.read_excel('iris.xlsx')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal.length  150 non-null    float64\n",
      " 1   sepal.width   150 non-null    float64\n",
      " 2   petal.length  150 non-null    float64\n",
      " 3   petal.width   150 non-null    float64\n",
      " 4   variety       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that, we have 150 records and 5 columns. Among, them 4 columns (sepal.length, sepal.width, petal.length, petal.width) are of float type i.e numeric whereas one (variety) is object i.e string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal.length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal.width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal.length</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal.width</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std  min  25%   50%  75%  max\n",
       "sepal.length  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
       "sepal.width   150.0  3.057333  0.435866  2.0  2.8  3.00  3.3  4.4\n",
       "petal.length  150.0  3.758000  1.765298  1.0  1.6  4.35  5.1  6.9\n",
       "petal.width   150.0  1.199333  0.762238  0.1  0.3  1.30  1.8  2.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The describe function give us a quick descriptive analysis of the dataframe. Here, we see that, the highest value in our dataset is 7.9 i.e of sepal length whereas the lowest value is 0.1 of petal.width. Seeing the maximum and minimum values we might be able to say that, there isn't many outliers in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal.length    0\n",
       "sepal.width     0\n",
       "petal.length    0\n",
       "petal.width     0\n",
       "variety         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal.length    0\n",
       "sepal.width     0\n",
       "petal.length    0\n",
       "petal.width     0\n",
       "variety         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not have any null or NaN values in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    149\n",
       "True       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have one duplicated value. We can remove it as it will not further add any kind of additional value while training our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing the duplicate values form the dataset\n",
    "iris.drop_duplicates(inplace = True)\n",
    "iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Handeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='sepal.width'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(2,2,1)\n",
    "sns.boxplot(y='petal.length',data=iris)\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(y='petal.width',data=iris)\n",
    "plt.subplot(2,2,3)\n",
    "sns.boxplot(y='sepal.length',data=iris)\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(y='sepal.width',data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see no outliers in our dataset except with the sepal.width. From the box plot, we can see that, the outliers present are only 4 and are not far away from the maximum and mimimum values. And, we only have 149 number of records, so we cannot afford to again reduce our dataset as it already has few number of records to train the model with. So, for that reason we are going to ignore the outliers.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Distribution of Dataset using Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_columns = ['sepal.length', 'sepal.width', 'petal.length', 'petal.width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(iris_columns)):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    sns.histplot(data=iris, x=iris_columns[i], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal.length</th>\n",
       "      <td>0.312826</td>\n",
       "      <td>-0.569006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal.width</th>\n",
       "      <td>0.307149</td>\n",
       "      <td>0.226236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal.length</th>\n",
       "      <td>-0.263101</td>\n",
       "      <td>-1.408270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal.width</th>\n",
       "      <td>-0.090076</td>\n",
       "      <td>-1.339953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  skew  kurtosis\n",
       "sepal.length  0.312826 -0.569006\n",
       "sepal.width   0.307149  0.226236\n",
       "petal.length -0.263101 -1.408270\n",
       "petal.width  -0.090076 -1.339953"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution = iris.agg(['skew','kurtosis']).transpose()\n",
    "distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph and the values, we got for skewness and kurtosis, we can conform that, our dataset is normally distributed. We consider a distribution to be normal if the skewness is between -2 to +2 and kurtosis to be between -7 to +7. So, the values are well under that range, meaning they are normal and need no statistical treatment. \n",
    "\n",
    "### Taking an overall view of the dataset using pairplot : Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cf5921cdc0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 805x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(30,25)\n",
    "sns.pairplot(iris, hue='variety', diag_kind='hist', markers=['*',\"D\",\"o\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pairplot, we can see that, the variety Setosa can be classified easily taking any of the combination. However, it is not true for other two varities. The best distinction can be give by the petal.length and petal.width chart. This is only for 2 dimension but as the dimension or features increases, the model might have many other ways to accurately differenciate the varities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='variety', ylabel='sepal.width'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.violinplot(x='variety',y='petal.length',data=iris)\n",
    "plt.subplot(2,2,2)\n",
    "sns.violinplot(x='variety',y='petal.width',data=iris)\n",
    "plt.subplot(2,2,3)\n",
    "sns.violinplot(x='variety',y='sepal.length',data=iris)\n",
    "plt.subplot(2,2,4)\n",
    "sns.violinplot(x='variety',y='sepal.width',data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above violinplot shows us the distribution of every variety in each of the feature category. They are seen to be noramlly distributed. Which will be beneficial to train our model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nunique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal.length    35\n",
       "sepal.width     23\n",
       "petal.length    43\n",
       "petal.width     22\n",
       "variety          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now lets see the unique values we have.\n",
    "iris.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the target variable (Variety) further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Setosa', 'Versicolor', 'Virginica'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['variety'].unique()\n",
    "\n",
    "# we have only three classes we need to classify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Setosa        50\n",
       "Versicolor    50\n",
       "Virginica     49\n",
       "Name: variety, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['variety'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "iris['variety'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, they are equally present in our dataset. This will not cause the imbalance problem and will not make a model more biased towards one class. So, we do not have to implement technique like SMOTE for this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode the target variables i.e convert variety to numerical types\n",
    "\n",
    "We know that, the machine learning models, cannot handle the string/object type so we need to convert all the categorical variables or object type variables to float or int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal.length  149 non-null    float64\n",
      " 1   sepal.width   149 non-null    float64\n",
      " 2   petal.length  149 non-null    float64\n",
      " 3   petal.width   149 non-null    float64\n",
      " 4   variety       149 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we only have the variety i.e target column to be object. So, we need to change it to numerical. We have only one feature to change and it only have small amount of unique values. For that reason, we can use the map function otherwise if there were a lot of features need to be encoded we could have used LabelEncoding() or OneHotEncoder() from Sklearn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "varity_num = {'Setosa': 1, 'Versicolor': 2,'Virginica':3}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['variety'] = iris['variety'].map(varity_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal.length  149 non-null    float64\n",
      " 1   sepal.width   149 non-null    float64\n",
      " 2   petal.length  149 non-null    float64\n",
      " 3   petal.width   149 non-null    float64\n",
      " 4   variety       149 non-null    int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 11.1 KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width  variety\n",
       "111           6.4          2.7           5.3          1.9        3\n",
       "15            5.7          4.4           1.5          0.4        1\n",
       "148           6.2          3.4           5.4          2.3        3\n",
       "53            5.5          2.3           4.0          1.3        2\n",
       "56            6.3          3.3           4.7          1.6        2\n",
       "24            4.8          3.4           1.9          0.2        1\n",
       "46            5.1          3.8           1.6          0.2        1\n",
       "96            5.7          2.9           4.2          1.3        2\n",
       "102           7.1          3.0           5.9          2.1        3\n",
       "26            5.0          3.4           1.6          0.4        1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sample(random_state=111, n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using co-relation matrix to see the relation amoung the featues with the target variable. \n",
    "\n",
    "Here, the four columns sepal.length, sepal.width, petal.length, petal.width are the feature columns and the variety is the target column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            color:  #000000;\n",
       "        }#T_dc15d_row4_col3{\n",
       "            background-color:  #7f0823;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_dc15d_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >sepal.length</th>        <th class=\"col_heading level0 col1\" >sepal.width</th>        <th class=\"col_heading level0 col2\" >petal.length</th>        <th class=\"col_heading level0 col3\" >petal.width</th>        <th class=\"col_heading level0 col4\" >variety</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_dc15d_level0_row0\" class=\"row_heading level0 row0\" >sepal.length</th>\n",
       "                        <td id=\"T_dc15d_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_dc15d_row0_col1\" class=\"data row0 col1\" >-0.118129</td>\n",
       "                        <td id=\"T_dc15d_row0_col2\" class=\"data row0 col2\" >0.873738</td>\n",
       "                        <td id=\"T_dc15d_row0_col3\" class=\"data row0 col3\" >0.820620</td>\n",
       "                        <td id=\"T_dc15d_row0_col4\" class=\"data row0 col4\" >0.786971</td>\n",
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       "                        <th id=\"T_dc15d_level0_row1\" class=\"row_heading level0 row1\" >sepal.width</th>\n",
       "                        <td id=\"T_dc15d_row1_col0\" class=\"data row1 col0\" >-0.118129</td>\n",
       "                        <td id=\"T_dc15d_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_dc15d_row1_col2\" class=\"data row1 col2\" >-0.426028</td>\n",
       "                        <td id=\"T_dc15d_row1_col3\" class=\"data row1 col3\" >-0.362894</td>\n",
       "                        <td id=\"T_dc15d_row1_col4\" class=\"data row1 col4\" >-0.422987</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc15d_level0_row2\" class=\"row_heading level0 row2\" >petal.length</th>\n",
       "                        <td id=\"T_dc15d_row2_col0\" class=\"data row2 col0\" >0.873738</td>\n",
       "                        <td id=\"T_dc15d_row2_col1\" class=\"data row2 col1\" >-0.426028</td>\n",
       "                        <td id=\"T_dc15d_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_dc15d_row2_col3\" class=\"data row2 col3\" >0.962772</td>\n",
       "                        <td id=\"T_dc15d_row2_col4\" class=\"data row2 col4\" >0.949402</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc15d_level0_row3\" class=\"row_heading level0 row3\" >petal.width</th>\n",
       "                        <td id=\"T_dc15d_row3_col0\" class=\"data row3 col0\" >0.820620</td>\n",
       "                        <td id=\"T_dc15d_row3_col1\" class=\"data row3 col1\" >-0.362894</td>\n",
       "                        <td id=\"T_dc15d_row3_col2\" class=\"data row3 col2\" >0.962772</td>\n",
       "                        <td id=\"T_dc15d_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_dc15d_row3_col4\" class=\"data row3 col4\" >0.956514</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dc15d_level0_row4\" class=\"row_heading level0 row4\" >variety</th>\n",
       "                        <td id=\"T_dc15d_row4_col0\" class=\"data row4 col0\" >0.786971</td>\n",
       "                        <td id=\"T_dc15d_row4_col1\" class=\"data row4 col1\" >-0.422987</td>\n",
       "                        <td id=\"T_dc15d_row4_col2\" class=\"data row4 col2\" >0.949402</td>\n",
       "                        <td id=\"T_dc15d_row4_col3\" class=\"data row4 col3\" >0.956514</td>\n",
       "                        <td id=\"T_dc15d_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1cf59ec2070>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = iris.corr(method='pearson')\n",
    "df.style.background_gradient(cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, the feature variables are highly co-realted with the target variable. Meaning we do not have to remove any columns and keeping them which will allow the models to extract the relation that a simple heatmap cannot show to us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Iris Dataset to be used in both the Experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.to_csv('iris_final.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width  variety\n",
       "0           5.1          3.5           1.4          0.2        1\n",
       "1           4.9          3.0           1.4          0.2        1\n",
       "2           4.7          3.2           1.3          0.2        1\n",
       "3           4.6          3.1           1.5          0.2        1\n",
       "4           5.0          3.6           1.4          0.2        1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris[['sepal.length', 'sepal.width', 'petal.length', 'petal.width']]\n",
    "y= iris['variety']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling: \n",
    "\n",
    "Feature Scaling helps us to standarize all the features we have into one scale. It is a standard practise to do as it will remove any kind of abnormality in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.898033</td>\n",
       "      <td>1.012401</td>\n",
       "      <td>-1.333255</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.139562</td>\n",
       "      <td>-0.137353</td>\n",
       "      <td>-1.333255</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.381091</td>\n",
       "      <td>0.322549</td>\n",
       "      <td>-1.390014</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.501855</td>\n",
       "      <td>0.092598</td>\n",
       "      <td>-1.276496</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.018798</td>\n",
       "      <td>1.242352</td>\n",
       "      <td>-1.333255</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width\n",
       "0     -0.898033     1.012401     -1.333255    -1.308624\n",
       "1     -1.139562    -0.137353     -1.333255    -1.308624\n",
       "2     -1.381091     0.322549     -1.390014    -1.308624\n",
       "3     -1.501855     0.092598     -1.276496    -1.308624\n",
       "4     -1.018798     1.242352     -1.333255    -1.308624"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: variety, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 1: \n",
    "\n",
    "### Spliting Dataset into 60-20-20 ratio:\n",
    "\n",
    "60% --> Training\n",
    "20% --> Validation\n",
    "20% --> Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.4,random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6\n",
      "0.2\n",
      "0.2\n"
     ]
    }
   ],
   "source": [
    "for dataset in [y_train, y_val, y_test]:\n",
    "    print(round(len(dataset) / len(y), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are able to successfully split the dataset into train, validation and training dataset into 60, 20, 20 ratio. As, per the first experiment requirement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Implementation:\n",
    "\n",
    "Since it is a classification problem. For the model implementation, we have choosed three classification models: \n",
    "1. Random Forest, \n",
    "2. Logistic Regression and \n",
    "3. Support Vector Machine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all the necessary modules\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization of the Models with random_state and other necessary Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(random_state=111,max_iter=110)\n",
    "svc = SVC(random_state=111)\n",
    "rf = RandomForestClassifier(random_state=111)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross_val_score for each of the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Logistic Regression:  0.956\n",
      "\n",
      " All 5 Cross validation scores: [0.88888889 0.94444444 0.94444444 1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator=lr,X = X_train, y = y_train, cv=5)\n",
    "\n",
    "print(\"The average cross validation score for Logistic Regression: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Support Vector Classifier:  0.967\n",
      "\n",
      " All 5 Cross validation scores: [0.94444444 0.94444444 0.94444444 1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator=svc,X = X_train, y = y_train, cv=5)\n",
    "\n",
    "print(\"The average cross validation score for Support Vector Classifier: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Random Forest Classifier:  0.933\n",
      "\n",
      " All 5 Cross validation scores: [0.88888889 0.83333333 0.94444444 1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator=rf,X = X_train, y = y_train, cv=5)\n",
    "round(scores.mean(),4)\n",
    "\n",
    "print(\"The average cross validation score for Random Forest Classifier: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, with the initial default parameter, we were able to get the highest score for Support Vector Classifier i.e 0.967. \n",
    "\n",
    "We can improve this model along with other using doing hyperparameter tuning. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing Grid Search for the Optimization of the Hyperparameters:\n",
    "\n",
    "Here, we provide a dictionary of different parameters which we can change in our model and try to figure out which combination of parameters will give us the best result/model with best accuracy, precision and recall. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(score):\n",
    "\n",
    "    means = score.cv_results_['mean_test_score']\n",
    "    sd = score.cv_results_['std_test_score']\n",
    "    \n",
    "    print('Score: {} \\n BEST PARAMS: {}\\n \\n'.format(round(score.best_score_,5),score.best_params_))\n",
    "\n",
    "    \n",
    "    for mean, std, params in zip(means, sd, score.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 2), round(std * 2, 2), params))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Logistic Regression: \n",
    "\n",
    "Here we are only giving the C value. It helps in regularization. Smaller the C value stronger is the regularization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.97778 \n",
      " BEST PARAMS: {'C': 10}\n",
      " \n",
      "\n",
      "0.71 (+/-0.04) for {'C': 0.001}\n",
      "0.73 (+/-0.09) for {'C': 0.01}\n",
      "0.86 (+/-0.2) for {'C': 0.1}\n",
      "0.96 (+/-0.08) for {'C': 1}\n",
      "0.98 (+/-0.05) for {'C': 10}\n",
      "0.95 (+/-0.09) for {'C': 100}\n",
      "0.94 (+/-0.1) for {'C': 1000}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "}\n",
    "\n",
    "lr_model = GridSearchCV(lr, parameters, cv=5)\n",
    "lr_model.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(lr_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the C parameter being 10. We have improved our score from 0.956 to 0.978."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, max_iter=110, random_state=111)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model_exp1 = lr_model.best_estimator_\n",
    "lr_model_exp1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Support Vector Classifier: \n",
    "\n",
    "We will be giving the kernel and the C values. The combination of these will give us better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.97778 \n",
      " BEST PARAMS: {'C': 10, 'kernel': 'poly'}\n",
      " \n",
      "\n",
      "0.94 (+/-0.1) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.74 (+/-0.15) for {'C': 0.1, 'kernel': 'rbf'}\n",
      "0.81 (+/-0.19) for {'C': 0.1, 'kernel': 'poly'}\n",
      "0.96 (+/-0.04) for {'C': 1, 'kernel': 'linear'}\n",
      "0.97 (+/-0.05) for {'C': 1, 'kernel': 'rbf'}\n",
      "0.89 (+/-0.16) for {'C': 1, 'kernel': 'poly'}\n",
      "0.95 (+/-0.09) for {'C': 10, 'kernel': 'linear'}\n",
      "0.94 (+/-0.1) for {'C': 10, 'kernel': 'rbf'}\n",
      "0.98 (+/-0.09) for {'C': 10, 'kernel': 'poly'}\n",
      "0.95 (+/-0.09) for {'C': 15, 'kernel': 'linear'}\n",
      "0.96 (+/-0.08) for {'C': 15, 'kernel': 'rbf'}\n",
      "0.98 (+/-0.09) for {'C': 15, 'kernel': 'poly'}\n",
      "0.94 (+/-0.07) for {'C': 20, 'kernel': 'linear'}\n",
      "0.96 (+/-0.08) for {'C': 20, 'kernel': 'rbf'}\n",
      "0.94 (+/-0.1) for {'C': 20, 'kernel': 'poly'}\n",
      "0.94 (+/-0.1) for {'C': 50, 'kernel': 'linear'}\n",
      "0.93 (+/-0.09) for {'C': 50, 'kernel': 'rbf'}\n",
      "0.94 (+/-0.1) for {'C': 50, 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'kernel': ['linear', 'rbf','poly'],\n",
    "    'C': [0.1, 1, 10,15,20,50],\n",
    "}\n",
    "\n",
    "svc_model = GridSearchCV(svc, parameters, cv=5)\n",
    "svc_model.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(svc_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here also we were able to increase our score from 0.967 to 0.978."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, kernel='poly', random_state=111)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model_exp1 = svc_model.best_estimator_\n",
    "svc_model_exp1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Random Forest:\n",
    "\n",
    "We have a lot of parameter we can tune for random forest but we are only taking n_estimators, max_depth,max_features, min_sample_split, and min_sample_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.97778 \n",
      " BEST PARAMS: {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      " \n",
      "\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.07) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.13) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.92 (+/-0.11) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.92 (+/-0.09) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.07) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.07) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.07) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
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      "0.94 (+/-0.07) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.91 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.91 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.07) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.13) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.91 (+/-0.09) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.92 (+/-0.09) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.07) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.91 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.91 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.98 (+/-0.05) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.96 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'n_estimators': [2,5, 50, 200],\n",
    "    'max_depth': [2,4, 8, 16, 32, None],\n",
    "    'max_features' : ['auto', 'sqrt'],\n",
    "    'min_samples_split' : [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "}\n",
    "\n",
    "rf_model = GridSearchCV(rf, parameters, cv=5)\n",
    "rf_model.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(rf_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=4, n_estimators=5, random_state=111)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_exp1 = rf_model.best_estimator_\n",
    "rf_model_exp1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here for the random forest also we were able to increase our score from 0.933 to 0.978. The Max_depth of 4 and n_estimator of 5 we are able to get the score of 0.978 while other parameter take the default values.\n",
    "\n",
    "Interesting thing to be noted is the model which we got after hyperparameter tuning all have same accuracy. It can imply that, they all were able to correctly predict the result leaving 1 or 2 data. \n",
    "\n",
    "There will be more clear distinction amoung the models after we have test them with the validation set in the model performace  phase with unknown data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Performance\n",
    "\n",
    "Here for the model performance we are going to take different classification matrix like accuracy, precision score, recall, confusion matrix and to sum them all we are going to create a classification report which will contain all the necessary score we need in one place.\n",
    "\n",
    "As, we know that, above we have mapped our variety as:\n",
    "\n",
    "- Setosa: 1\n",
    "- Versicolor: 2\n",
    "- Virginica: 3\n",
    "\n",
    "Here, the most important metrics are :\n",
    "\n",
    "Accuracy: How accurate is the model able to predict the values. --> (TP + TN)/(TP + TN + FP + FN)\n",
    "\n",
    "Precision: When the model **predicted** True, how often it was right? --> TP / (TP+FP). eg: when a model said the patient has cancer but in reality it did not have it\n",
    "\n",
    "Recall: When the class was **actually** True, how often did the classifer get it right.  ---> TP/(TP + FN) eg: when a model said the patiend did not have cancer but in reality they did have it\n",
    "\n",
    "F1-Score: It is the harmonic mean of both recall and precision. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(name, model, features, labels):\n",
    "    start = time()\n",
    "    pred = model.predict(features)\n",
    "    end = time()\n",
    "    accuracy = round(accuracy_score(labels, pred), 4)\n",
    "    precision = round(precision_score(labels, pred,average='weighted'), 4)\n",
    "    recall = round(recall_score(labels, pred, average='weighted'), 4)\n",
    "    confusion_m = confusion_matrix(labels, pred)\n",
    "    print('{} -- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}ms'.format(name,\n",
    "                                                                                   accuracy,\n",
    "                                                          \n",
    "                                                                                   precision,\n",
    "                                                                                   recall,\n",
    "                                                                                   round((end - start)*1000, 1)))\n",
    "    print('\\n')\n",
    "    print(confusion_m)\n",
    "    print('\\n')\n",
    "    print(classification_report(labels,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       1.00      1.00      1.00        13\n",
      "           3       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Logistic Regression', lr_model_exp1, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that, the model was able to accurately predict all the three class we had. So, all the matrix are 1 along with that the latency of the model is 1ms. Logistic Regression being a simple model was able to accurately predict all the classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier -- Accuracy: 0.9667 / Precision: 0.969 / Recall: 0.9667 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  1  5]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       0.93      1.00      0.96        13\n",
      "           3       1.00      0.83      0.91         6\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.98      0.94      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Support Vector Classifier', svc_model_exp1, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The support vector classifier was not able to accurately predict all the three classes. It was able to correctly predict the class 1 i.e Setosa. \n",
    "\n",
    "From the confusion matrix, we can see, one class was predicted incorrectly. The matrix can be interperated as, the true value was Virginica but the model predicted it to be Versicolor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 2.0ms\n",
      "\n",
      "\n",
      "[[11  0  0]\n",
      " [ 0 13  0]\n",
      " [ 0  0  6]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00        11\n",
      "           2       1.00      1.00      1.00        13\n",
      "           3       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Random Forest Classifier', rf_model_exp1, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as logistic regression, Random Forest Classifier was able to accurately classify all the classes. However, the latency of 2.0ms was observed in Random Forest which is 2X the latency of Logistic Regression. \n",
    "\n",
    "So, for the final model development and model test we will be using the Logistic Regression Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Logistic Regression model with the test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression -- Accuracy: 0.9333 / Precision: 0.9436 / Recall: 0.9333 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[ 8  0  0]\n",
      " [ 0  9  2]\n",
      " [ 0  0 11]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         8\n",
      "           2       1.00      0.82      0.90        11\n",
      "           3       0.85      1.00      0.92        11\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.95      0.94      0.94        30\n",
      "weighted avg       0.94      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Logistic Regression', lr_model_exp1, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, in the test data set with logistic regression, we can see that, the model was able to accurately predict the class 1 i.e Setosa. But was not able to predict the class 2 i.e Versicolor accurately. The 2 data were inaccurately classified as Virginica but in reality they were versicolor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment 2: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the only difference in our whole experiment will be the amount of the data we send to training, validation and testing. All the other process will be same. We will be using the same data X and y which we have used above that had been scaled. The models will be same, the model comparision process will be same.\n",
    "\n",
    "The top 5 rows can be seen below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.018798</td>\n",
       "      <td>1.242352</td>\n",
       "      <td>-1.333255</td>\n",
       "      <td>-1.308624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal.length  sepal.width  petal.length  petal.width\n",
       "0     -0.898033     1.012401     -1.333255    -1.308624\n",
       "1     -1.139562    -0.137353     -1.333255    -1.308624\n",
       "2     -1.381091     0.322549     -1.390014    -1.308624\n",
       "3     -1.501855     0.092598     -1.276496    -1.308624\n",
       "4     -1.018798     1.242352     -1.333255    -1.308624"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: variety, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 4)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting Dataset into 80-10-10 ratio:\n",
    "\n",
    "80% --> Training\n",
    "10% --> Validation\n",
    "10% --> Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=111)\n",
    "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "0.1\n",
      "0.1\n"
     ]
    }
   ],
   "source": [
    "for dataset in [y_train, y_val, y_test]:\n",
    "    print(round(len(dataset) / len(y), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization of the Models with random_state and other necessary Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(random_state=111,max_iter=110)\n",
    "svc = SVC(random_state=111)\n",
    "rf = RandomForestClassifier(random_state=111)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cross_val_score for each of the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Logistic Regression:  0.942\n",
      "\n",
      " All 5 Cross validation scores: [0.95833333 0.875      0.91666667 0.95833333 1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator= lr,X = X_train, y = y_train, cv=5)\n",
    "\n",
    "print(\"The average cross validation score for Logistic Regression: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Support Vector Classifier:  0.958\n",
      "\n",
      " All 5 Cross validation scores: [1.         0.91666667 0.91666667 0.95833333 1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator= svc,X = X_train, y = y_train, cv=5)\n",
    "\n",
    "print(\"The average cross validation score for Support Vector Classifier: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average cross validation score for Random Forest Classifier:  0.933\n",
      "\n",
      " All 5 Cross validation scores: [0.95833333 0.91666667 0.91666667 0.875      1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator= rf,X = X_train, y = y_train, cv=5)\n",
    "round(scores.mean(),4)\n",
    "\n",
    "print(\"The average cross validation score for Random Forest Classifier: \",round(scores.mean(),3))\n",
    "print()\n",
    "print(\" All 5 Cross validation scores:\",scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the cross validation score we found the the support vector classifier gives us the highest score followed by Logistic Regression and Random Forest Classifier. \n",
    "\n",
    "We can increase the score using the grid search by optimizing the hyper-parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing Grid Search for the Optimization of the Hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(score):\n",
    "\n",
    "    means = score.cv_results_['mean_test_score']\n",
    "    sd = score.cv_results_['std_test_score']\n",
    "    \n",
    "    print('Score: {} \\n BEST PARAMS: {}\\n \\n'.format(round(score.best_score_,5),score.best_params_))\n",
    "\n",
    "    \n",
    "    for mean, std, params in zip(means, sd, score.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 2), round(std * 2, 2), params))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Logistic Regression: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.96667 \n",
      " BEST PARAMS: {'C': 10}\n",
      " \n",
      "\n",
      "0.77 (+/-0.18) for {'C': 0.001}\n",
      "0.87 (+/-0.06) for {'C': 0.01}\n",
      "0.91 (+/-0.06) for {'C': 0.1}\n",
      "0.94 (+/-0.08) for {'C': 1}\n",
      "0.97 (+/-0.03) for {'C': 10}\n",
      "0.97 (+/-0.03) for {'C': 100}\n",
      "0.96 (+/-0.05) for {'C': 1000}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "}\n",
    "\n",
    "lr_model2 = GridSearchCV(lr, parameters, cv=5)\n",
    "lr_model2.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(lr_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, max_iter=110, random_state=111)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model_exp2 = lr_model2.best_estimator_\n",
    "lr_model_exp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the accuracy score has increased to 0.9667 from 0.942 by only changing the C value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Support Vector Classifier: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.975 \n",
      " BEST PARAMS: {'C': 20, 'kernel': 'linear'}\n",
      " \n",
      "\n",
      "0.96 (+/-0.07) for {'C': 0.1, 'kernel': 'linear'}\n",
      "0.9 (+/-0.07) for {'C': 0.1, 'kernel': 'rbf'}\n",
      "0.84 (+/-0.11) for {'C': 0.1, 'kernel': 'poly'}\n",
      "0.96 (+/-0.05) for {'C': 1, 'kernel': 'linear'}\n",
      "0.96 (+/-0.07) for {'C': 1, 'kernel': 'rbf'}\n",
      "0.9 (+/-0.08) for {'C': 1, 'kernel': 'poly'}\n",
      "0.96 (+/-0.0) for {'C': 10, 'kernel': 'linear'}\n",
      "0.96 (+/-0.05) for {'C': 10, 'kernel': 'rbf'}\n",
      "0.95 (+/-0.08) for {'C': 10, 'kernel': 'poly'}\n",
      "0.97 (+/-0.03) for {'C': 15, 'kernel': 'linear'}\n",
      "0.96 (+/-0.05) for {'C': 15, 'kernel': 'rbf'}\n",
      "0.97 (+/-0.1) for {'C': 15, 'kernel': 'poly'}\n",
      "0.98 (+/-0.04) for {'C': 20, 'kernel': 'linear'}\n",
      "0.96 (+/-0.05) for {'C': 20, 'kernel': 'rbf'}\n",
      "0.97 (+/-0.1) for {'C': 20, 'kernel': 'poly'}\n",
      "0.98 (+/-0.04) for {'C': 50, 'kernel': 'linear'}\n",
      "0.93 (+/-0.08) for {'C': 50, 'kernel': 'rbf'}\n",
      "0.93 (+/-0.11) for {'C': 50, 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'kernel': ['linear', 'rbf','poly'],\n",
    "    'C': [0.1, 1, 10,15,20,50],\n",
    "}\n",
    "\n",
    "svc_model2 = GridSearchCV(svc, parameters, cv=5)\n",
    "svc_model2.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(svc_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=20, kernel='linear', random_state=111)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model_exp2 = svc_model2.best_estimator_\n",
    "svc_model_exp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, seen in Logistic Regression, the same can be seen in Support Vector Classifier. The score has increased to 0.975 from 0.958."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.95 \n",
      " BEST PARAMS: {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      " \n",
      "\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.92 (+/-0.1) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
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      "0.93 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.12) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': 32, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.92 (+/-0.06) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.92 (+/-0.12) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.12) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.92 (+/-0.06) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.92 (+/-0.12) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.12) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 5}\n",
      "0.92 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 50}\n",
      "0.93 (+/-0.1) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "0.95 (+/-0.06) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 2}\n",
      "0.94 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 5}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 50}\n",
      "0.93 (+/-0.08) for {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'n_estimators': [2,5, 50, 200],\n",
    "    'max_depth': [2,4, 8, 16, 32, None],\n",
    "    'max_features' : ['auto', 'sqrt'],\n",
    "    'min_samples_split' : [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "}\n",
    "\n",
    "rf_model2 = GridSearchCV(rf, parameters, cv=5)\n",
    "rf_model2.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print_results(rf_model2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=2, n_estimators=2, random_state=111)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_exp2 = rf_model2.best_estimator_\n",
    "rf_model_exp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest classifier is the least performing model with a score of 0.95. However, its score has also increase after the hyper parameter optimization. Which means the hyper-parameter tuning had done it job.\n",
    "\n",
    "From here, we can see that, the SVC model performed best in compared to other after hyper-parameter tuning. Now, we need to evaluate the models performace with the validation data and see which model is best to generalize the unseen validation data. After that, we will be able to select the best model and again get a unbiased view after testing with test dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(name, model, features, labels):\n",
    "    start = time()\n",
    "    pred = model.predict(features)\n",
    "    end = time()\n",
    "    accuracy = round(accuracy_score(labels, pred), 4)\n",
    "    precision = round(precision_score(labels, pred,average='weighted'), 4)\n",
    "    recall = round(recall_score(labels, pred, average='weighted'), 4)\n",
    "    confusion_m = confusion_matrix(labels, pred)\n",
    "    print('{} -- Accuracy: {} / Precision: {} / Recall: {} / Latency: {}ms'.format(name,\n",
    "                                                                                   accuracy,\n",
    "                                                          \n",
    "                                                                                   precision,\n",
    "                                                                                   recall,\n",
    "                                                                                   round((end - start)*1000, 1)))\n",
    "    print('\\n')\n",
    "    print(confusion_m)\n",
    "    print('\\n')\n",
    "    print(classification_report(labels,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[5 0 0]\n",
      " [0 5 0]\n",
      " [0 0 5]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         5\n",
      "           2       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           1.00        15\n",
      "   macro avg       1.00      1.00      1.00        15\n",
      "weighted avg       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Logistic Regression', lr_model_exp2, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[5 0 0]\n",
      " [0 5 0]\n",
      " [0 0 5]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         5\n",
      "           2       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           1.00        15\n",
      "   macro avg       1.00      1.00      1.00        15\n",
      "weighted avg       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Support Vector Classifier', svc_model_exp2, X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest -- Accuracy: 0.9333 / Precision: 0.9444 / Recall: 0.9333 / Latency: 2.0ms\n",
      "\n",
      "\n",
      "[[5 0 0]\n",
      " [0 5 0]\n",
      " [1 0 4]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.83      1.00      0.91         5\n",
      "           2       1.00      1.00      1.00         5\n",
      "           3       1.00      0.80      0.89         5\n",
      "\n",
      "    accuracy                           0.93        15\n",
      "   macro avg       0.94      0.93      0.93        15\n",
      "weighted avg       0.94      0.93      0.93        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Random Forest', rf_model_exp2, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, the Logistic Regression and SVC model performed the best as they were able to accurately predict the validation dataset without any mistake giving the accuracy, recall and precision of value 1. However, it cannot be said for random forest classifer. It incorrectly predicted 1 data point to be Setosa inplace of Virginica. \n",
    "\n",
    "As, we know that, in above training set, SVC was able to accurately generalize the data with the training set giving us the best result. So, we will be choosing the SVC modle for our final deployment and testing with test data to get unbiased overall view of the model.\n",
    "\n",
    "### SVC model testing with test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier -- Accuracy: 1.0 / Precision: 1.0 / Recall: 1.0 / Latency: 1.0ms\n",
      "\n",
      "\n",
      "[[5 0 0]\n",
      " [0 4 0]\n",
      " [0 0 6]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00         5\n",
      "           2       1.00      1.00      1.00         4\n",
      "           3       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           1.00        15\n",
      "   macro avg       1.00      1.00      1.00        15\n",
      "weighted avg       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_model('Support Vector Classifier', svc_model_exp2, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The svc model was able to accurately classify each and every class of variety. The recall, precision and accuracy was 1. This means the model was accurately able to generalize the unseen data and give a good prediction. There is no overfitting or underfitting of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the performace of models from two sets of experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Latency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LR_exp1</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC_exp1</th>\n",
       "      <td>0.96670</td>\n",
       "      <td>0.9690</td>\n",
       "      <td>0.9670</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_exp1</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_LR_exp1</th>\n",
       "      <td>0.93333</td>\n",
       "      <td>0.9436</td>\n",
       "      <td>0.9333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR_exp2</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC_exp2</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_exp2</th>\n",
       "      <td>0.93330</td>\n",
       "      <td>0.9444</td>\n",
       "      <td>0.9333</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_SVC_exp2</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Accuracy  Precision  Recall  Latency\n",
       "LR_exp1         1.00000     1.0000  1.0000      1.0\n",
       "SVC_exp1        0.96670     0.9690  0.9670      1.8\n",
       "RF_exp1         1.00000     1.0000  1.0000      2.0\n",
       "Test_LR_exp1    0.93333     0.9436  0.9333      1.0\n",
       "LR_exp2         1.00000     1.0000  1.0000      2.0\n",
       "SVC_exp2        1.00000     1.0000  1.0000      2.0\n",
       "RF_exp2         0.93330     0.9444  0.9333      3.0\n",
       "Test_SVC_exp2   1.00000     1.0000  1.0000      1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.DataFrame(data = [[1,0.9667,1,0.93333,1,1,0.9333,1],[1,0.969,1,0.9436,1,1,0.9444,1],[1,0.967,1,0.9333,1,1,0.9333,1],[1,1.8,2,1,2,2,3,1]],columns=['LR_exp1','SVC_exp1','RF_exp1','Test_LR_exp1','LR_exp2','SVC_exp2','RF_exp2','Test_SVC_exp2'],index=['Accuracy','Precision','Recall','Latency'])\n",
    "series.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have accuracy, precision, recall and latency of all the model we tested in this experiment. The suffix tells us which experiment i.e 1 or 2. The prefix tells us about the model implemented to get that result and the \"test\" in from of some the models meaning that they were selected and test dataset were applied on them.\n",
    "\n",
    "We can generalize all this by looking at only the Test_LR_exp1 and Test_SVC_exp2. Here, in the exp1 we found that, the LR gave the best result and in exp2 the SVC model gave the best result amoung all the models we created in there respective experiments. So, when the unseen biased data was tested with on LR in the exp1. It was not able to accurately classify every data meaning was not able to generalize the unseen data to the model. However, in the exp2 the SVC model was able to generalize each and every data and getting a accuracy score of 1. \n",
    "\n",
    "### Conclusion:\n",
    "\n",
    "This means that, the model SVC from exp2 is good model than LR from exp1. Making the second experiment choice i.e splitting the dataset into **80-10-10** was good to get overall generalized model. \n",
    "\n",
    "The second experiment had a greater number of training dataset which might be the reason it gave us a good model rather than the first experiment which had only 60% of data to train the model. "
   ]
  }
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