diff --git a/week 2/priyatam75_week2.ipynb b/week 2/priyatam75_week2.ipynb
new file mode 100644
index 0000000..a111c45
--- /dev/null
+++ b/week 2/priyatam75_week2.ipynb	
@@ -0,0 +1,641 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WEEK-2"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Learn about Counter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Counter is a class in the collections module of python, it is genearlly used to count hashable objects. it automatically creates a hash table when an iterable is invoked.\n",
+    "the most_common() methode is used to display the elements of object having higher frequency compared to other elements.\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Implementing with Iris Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the dataset has been downloaded from kaggle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Id</th>\n",
+       "      <th>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
+       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
+       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
+       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
+       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data=pd.read_csv('Iris.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting into x and y\n",
+    "X=data.iloc[:,0:5].values\n",
+    "Y=data.iloc[:,5].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.00e+00 5.10e+00 3.50e+00 1.40e+00 2.00e-01]\n",
+      " [2.00e+00 4.90e+00 3.00e+00 1.40e+00 2.00e-01]\n",
+      " [3.00e+00 4.70e+00 3.20e+00 1.30e+00 2.00e-01]\n",
+      " [4.00e+00 4.60e+00 3.10e+00 1.50e+00 2.00e-01]\n",
+      " [5.00e+00 5.00e+00 3.60e+00 1.40e+00 2.00e-01]\n",
+      " [6.00e+00 5.40e+00 3.90e+00 1.70e+00 4.00e-01]\n",
+      " [7.00e+00 4.60e+00 3.40e+00 1.40e+00 3.00e-01]\n",
+      " [8.00e+00 5.00e+00 3.40e+00 1.50e+00 2.00e-01]\n",
+      " [9.00e+00 4.40e+00 2.90e+00 1.40e+00 2.00e-01]\n",
+      " [1.00e+01 4.90e+00 3.10e+00 1.50e+00 1.00e-01]\n",
+      " [1.10e+01 5.40e+00 3.70e+00 1.50e+00 2.00e-01]\n",
+      " [1.20e+01 4.80e+00 3.40e+00 1.60e+00 2.00e-01]\n",
+      " [1.30e+01 4.80e+00 3.00e+00 1.40e+00 1.00e-01]\n",
+      " [1.40e+01 4.30e+00 3.00e+00 1.10e+00 1.00e-01]\n",
+      " [1.50e+01 5.80e+00 4.00e+00 1.20e+00 2.00e-01]\n",
+      " [1.60e+01 5.70e+00 4.40e+00 1.50e+00 4.00e-01]\n",
+      " [1.70e+01 5.40e+00 3.90e+00 1.30e+00 4.00e-01]\n",
+      " [1.80e+01 5.10e+00 3.50e+00 1.40e+00 3.00e-01]\n",
+      " [1.90e+01 5.70e+00 3.80e+00 1.70e+00 3.00e-01]\n",
+      " [2.00e+01 5.10e+00 3.80e+00 1.50e+00 3.00e-01]\n",
+      " [2.10e+01 5.40e+00 3.40e+00 1.70e+00 2.00e-01]\n",
+      " [2.20e+01 5.10e+00 3.70e+00 1.50e+00 4.00e-01]\n",
+      " [2.30e+01 4.60e+00 3.60e+00 1.00e+00 2.00e-01]\n",
+      " [2.40e+01 5.10e+00 3.30e+00 1.70e+00 5.00e-01]\n",
+      " [2.50e+01 4.80e+00 3.40e+00 1.90e+00 2.00e-01]\n",
+      " [2.60e+01 5.00e+00 3.00e+00 1.60e+00 2.00e-01]\n",
+      " [2.70e+01 5.00e+00 3.40e+00 1.60e+00 4.00e-01]\n",
+      " [2.80e+01 5.20e+00 3.50e+00 1.50e+00 2.00e-01]\n",
+      " [2.90e+01 5.20e+00 3.40e+00 1.40e+00 2.00e-01]\n",
+      " [3.00e+01 4.70e+00 3.20e+00 1.60e+00 2.00e-01]\n",
+      " [3.10e+01 4.80e+00 3.10e+00 1.60e+00 2.00e-01]\n",
+      " [3.20e+01 5.40e+00 3.40e+00 1.50e+00 4.00e-01]\n",
+      " [3.30e+01 5.20e+00 4.10e+00 1.50e+00 1.00e-01]\n",
+      " [3.40e+01 5.50e+00 4.20e+00 1.40e+00 2.00e-01]\n",
+      " [3.50e+01 4.90e+00 3.10e+00 1.50e+00 1.00e-01]\n",
+      " [3.60e+01 5.00e+00 3.20e+00 1.20e+00 2.00e-01]\n",
+      " [3.70e+01 5.50e+00 3.50e+00 1.30e+00 2.00e-01]\n",
+      " [3.80e+01 4.90e+00 3.10e+00 1.50e+00 1.00e-01]\n",
+      " [3.90e+01 4.40e+00 3.00e+00 1.30e+00 2.00e-01]\n",
+      " [4.00e+01 5.10e+00 3.40e+00 1.50e+00 2.00e-01]\n",
+      " [4.10e+01 5.00e+00 3.50e+00 1.30e+00 3.00e-01]\n",
+      " [4.20e+01 4.50e+00 2.30e+00 1.30e+00 3.00e-01]\n",
+      " [4.30e+01 4.40e+00 3.20e+00 1.30e+00 2.00e-01]\n",
+      " [4.40e+01 5.00e+00 3.50e+00 1.60e+00 6.00e-01]\n",
+      " [4.50e+01 5.10e+00 3.80e+00 1.90e+00 4.00e-01]\n",
+      " [4.60e+01 4.80e+00 3.00e+00 1.40e+00 3.00e-01]\n",
+      " [4.70e+01 5.10e+00 3.80e+00 1.60e+00 2.00e-01]\n",
+      " [4.80e+01 4.60e+00 3.20e+00 1.40e+00 2.00e-01]\n",
+      " [4.90e+01 5.30e+00 3.70e+00 1.50e+00 2.00e-01]\n",
+      " [5.00e+01 5.00e+00 3.30e+00 1.40e+00 2.00e-01]\n",
+      " [5.10e+01 7.00e+00 3.20e+00 4.70e+00 1.40e+00]\n",
+      " [5.20e+01 6.40e+00 3.20e+00 4.50e+00 1.50e+00]\n",
+      " [5.30e+01 6.90e+00 3.10e+00 4.90e+00 1.50e+00]\n",
+      " [5.40e+01 5.50e+00 2.30e+00 4.00e+00 1.30e+00]\n",
+      " [5.50e+01 6.50e+00 2.80e+00 4.60e+00 1.50e+00]\n",
+      " [5.60e+01 5.70e+00 2.80e+00 4.50e+00 1.30e+00]\n",
+      " [5.70e+01 6.30e+00 3.30e+00 4.70e+00 1.60e+00]\n",
+      " [5.80e+01 4.90e+00 2.40e+00 3.30e+00 1.00e+00]\n",
+      " [5.90e+01 6.60e+00 2.90e+00 4.60e+00 1.30e+00]\n",
+      " [6.00e+01 5.20e+00 2.70e+00 3.90e+00 1.40e+00]\n",
+      " [6.10e+01 5.00e+00 2.00e+00 3.50e+00 1.00e+00]\n",
+      " [6.20e+01 5.90e+00 3.00e+00 4.20e+00 1.50e+00]\n",
+      " [6.30e+01 6.00e+00 2.20e+00 4.00e+00 1.00e+00]\n",
+      " [6.40e+01 6.10e+00 2.90e+00 4.70e+00 1.40e+00]\n",
+      " [6.50e+01 5.60e+00 2.90e+00 3.60e+00 1.30e+00]\n",
+      " [6.60e+01 6.70e+00 3.10e+00 4.40e+00 1.40e+00]\n",
+      " [6.70e+01 5.60e+00 3.00e+00 4.50e+00 1.50e+00]\n",
+      " [6.80e+01 5.80e+00 2.70e+00 4.10e+00 1.00e+00]\n",
+      " [6.90e+01 6.20e+00 2.20e+00 4.50e+00 1.50e+00]\n",
+      " [7.00e+01 5.60e+00 2.50e+00 3.90e+00 1.10e+00]\n",
+      " [7.10e+01 5.90e+00 3.20e+00 4.80e+00 1.80e+00]\n",
+      " [7.20e+01 6.10e+00 2.80e+00 4.00e+00 1.30e+00]\n",
+      " [7.30e+01 6.30e+00 2.50e+00 4.90e+00 1.50e+00]\n",
+      " [7.40e+01 6.10e+00 2.80e+00 4.70e+00 1.20e+00]\n",
+      " [7.50e+01 6.40e+00 2.90e+00 4.30e+00 1.30e+00]\n",
+      " [7.60e+01 6.60e+00 3.00e+00 4.40e+00 1.40e+00]\n",
+      " [7.70e+01 6.80e+00 2.80e+00 4.80e+00 1.40e+00]\n",
+      " [7.80e+01 6.70e+00 3.00e+00 5.00e+00 1.70e+00]\n",
+      " [7.90e+01 6.00e+00 2.90e+00 4.50e+00 1.50e+00]\n",
+      " [8.00e+01 5.70e+00 2.60e+00 3.50e+00 1.00e+00]\n",
+      " [8.10e+01 5.50e+00 2.40e+00 3.80e+00 1.10e+00]\n",
+      " [8.20e+01 5.50e+00 2.40e+00 3.70e+00 1.00e+00]\n",
+      " [8.30e+01 5.80e+00 2.70e+00 3.90e+00 1.20e+00]\n",
+      " [8.40e+01 6.00e+00 2.70e+00 5.10e+00 1.60e+00]\n",
+      " [8.50e+01 5.40e+00 3.00e+00 4.50e+00 1.50e+00]\n",
+      " [8.60e+01 6.00e+00 3.40e+00 4.50e+00 1.60e+00]\n",
+      " [8.70e+01 6.70e+00 3.10e+00 4.70e+00 1.50e+00]\n",
+      " [8.80e+01 6.30e+00 2.30e+00 4.40e+00 1.30e+00]\n",
+      " [8.90e+01 5.60e+00 3.00e+00 4.10e+00 1.30e+00]\n",
+      " [9.00e+01 5.50e+00 2.50e+00 4.00e+00 1.30e+00]\n",
+      " [9.10e+01 5.50e+00 2.60e+00 4.40e+00 1.20e+00]\n",
+      " [9.20e+01 6.10e+00 3.00e+00 4.60e+00 1.40e+00]\n",
+      " [9.30e+01 5.80e+00 2.60e+00 4.00e+00 1.20e+00]\n",
+      " [9.40e+01 5.00e+00 2.30e+00 3.30e+00 1.00e+00]\n",
+      " [9.50e+01 5.60e+00 2.70e+00 4.20e+00 1.30e+00]\n",
+      " [9.60e+01 5.70e+00 3.00e+00 4.20e+00 1.20e+00]\n",
+      " [9.70e+01 5.70e+00 2.90e+00 4.20e+00 1.30e+00]\n",
+      " [9.80e+01 6.20e+00 2.90e+00 4.30e+00 1.30e+00]\n",
+      " [9.90e+01 5.10e+00 2.50e+00 3.00e+00 1.10e+00]\n",
+      " [1.00e+02 5.70e+00 2.80e+00 4.10e+00 1.30e+00]\n",
+      " [1.01e+02 6.30e+00 3.30e+00 6.00e+00 2.50e+00]\n",
+      " [1.02e+02 5.80e+00 2.70e+00 5.10e+00 1.90e+00]\n",
+      " [1.03e+02 7.10e+00 3.00e+00 5.90e+00 2.10e+00]\n",
+      " [1.04e+02 6.30e+00 2.90e+00 5.60e+00 1.80e+00]\n",
+      " [1.05e+02 6.50e+00 3.00e+00 5.80e+00 2.20e+00]\n",
+      " [1.06e+02 7.60e+00 3.00e+00 6.60e+00 2.10e+00]\n",
+      " [1.07e+02 4.90e+00 2.50e+00 4.50e+00 1.70e+00]\n",
+      " [1.08e+02 7.30e+00 2.90e+00 6.30e+00 1.80e+00]\n",
+      " [1.09e+02 6.70e+00 2.50e+00 5.80e+00 1.80e+00]\n",
+      " [1.10e+02 7.20e+00 3.60e+00 6.10e+00 2.50e+00]\n",
+      " [1.11e+02 6.50e+00 3.20e+00 5.10e+00 2.00e+00]\n",
+      " [1.12e+02 6.40e+00 2.70e+00 5.30e+00 1.90e+00]\n",
+      " [1.13e+02 6.80e+00 3.00e+00 5.50e+00 2.10e+00]\n",
+      " [1.14e+02 5.70e+00 2.50e+00 5.00e+00 2.00e+00]\n",
+      " [1.15e+02 5.80e+00 2.80e+00 5.10e+00 2.40e+00]\n",
+      " [1.16e+02 6.40e+00 3.20e+00 5.30e+00 2.30e+00]\n",
+      " [1.17e+02 6.50e+00 3.00e+00 5.50e+00 1.80e+00]\n",
+      " [1.18e+02 7.70e+00 3.80e+00 6.70e+00 2.20e+00]\n",
+      " [1.19e+02 7.70e+00 2.60e+00 6.90e+00 2.30e+00]\n",
+      " [1.20e+02 6.00e+00 2.20e+00 5.00e+00 1.50e+00]\n",
+      " [1.21e+02 6.90e+00 3.20e+00 5.70e+00 2.30e+00]\n",
+      " [1.22e+02 5.60e+00 2.80e+00 4.90e+00 2.00e+00]\n",
+      " [1.23e+02 7.70e+00 2.80e+00 6.70e+00 2.00e+00]\n",
+      " [1.24e+02 6.30e+00 2.70e+00 4.90e+00 1.80e+00]\n",
+      " [1.25e+02 6.70e+00 3.30e+00 5.70e+00 2.10e+00]\n",
+      " [1.26e+02 7.20e+00 3.20e+00 6.00e+00 1.80e+00]\n",
+      " [1.27e+02 6.20e+00 2.80e+00 4.80e+00 1.80e+00]\n",
+      " [1.28e+02 6.10e+00 3.00e+00 4.90e+00 1.80e+00]\n",
+      " [1.29e+02 6.40e+00 2.80e+00 5.60e+00 2.10e+00]\n",
+      " [1.30e+02 7.20e+00 3.00e+00 5.80e+00 1.60e+00]\n",
+      " [1.31e+02 7.40e+00 2.80e+00 6.10e+00 1.90e+00]\n",
+      " [1.32e+02 7.90e+00 3.80e+00 6.40e+00 2.00e+00]\n",
+      " [1.33e+02 6.40e+00 2.80e+00 5.60e+00 2.20e+00]\n",
+      " [1.34e+02 6.30e+00 2.80e+00 5.10e+00 1.50e+00]\n",
+      " [1.35e+02 6.10e+00 2.60e+00 5.60e+00 1.40e+00]\n",
+      " [1.36e+02 7.70e+00 3.00e+00 6.10e+00 2.30e+00]\n",
+      " [1.37e+02 6.30e+00 3.40e+00 5.60e+00 2.40e+00]\n",
+      " [1.38e+02 6.40e+00 3.10e+00 5.50e+00 1.80e+00]\n",
+      " [1.39e+02 6.00e+00 3.00e+00 4.80e+00 1.80e+00]\n",
+      " [1.40e+02 6.90e+00 3.10e+00 5.40e+00 2.10e+00]\n",
+      " [1.41e+02 6.70e+00 3.10e+00 5.60e+00 2.40e+00]\n",
+      " [1.42e+02 6.90e+00 3.10e+00 5.10e+00 2.30e+00]\n",
+      " [1.43e+02 5.80e+00 2.70e+00 5.10e+00 1.90e+00]\n",
+      " [1.44e+02 6.80e+00 3.20e+00 5.90e+00 2.30e+00]\n",
+      " [1.45e+02 6.70e+00 3.30e+00 5.70e+00 2.50e+00]\n",
+      " [1.46e+02 6.70e+00 3.00e+00 5.20e+00 2.30e+00]\n",
+      " [1.47e+02 6.30e+00 2.50e+00 5.00e+00 1.90e+00]\n",
+      " [1.48e+02 6.50e+00 3.00e+00 5.20e+00 2.00e+00]\n",
+      " [1.49e+02 6.20e+00 3.40e+00 5.40e+00 2.30e+00]\n",
+      " [1.50e+02 5.90e+00 3.00e+00 5.10e+00 1.80e+00]] ['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Srinivas\\Anaconda3\\envs\\myenvs\\lib\\site-packages\\sklearn\\utils\\deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
+      "  warnings.warn(msg, category=DeprecationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# trating missing values\n",
+    "from sklearn.preprocessing  import Imputer\n",
+    "imputer=Imputer(missing_values=\"NaN\",strategy=\"mean\", axis=0)\n",
+    "imputer=imputer.fit(X[:,0:5])\n",
+    "X[:,0:5]=imputer.transform(X[:,0:5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imputer is a predefined class in scikit-learn package,The instance created to imputer object is passed with the arguments of missing values, the strategy for replacement which can either be mean, median or any statistical substituion.\n",
+    "The instance created needs to be fitted to our data, this is accomplished by 'fit' method\n",
+    "finally the missing values in data are substituted with desired values with 'transform' method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "xtrain,xtest,ytrain,ytest= train_test_split(X, Y, test_size = 0.3333333, random_state =0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt\n",
+    "def euclidean_distance(row1, row2):\n",
+    "    distance = 0.0\n",
+    "    for i in range(len(row1)-1):\n",
+    "        distance += ((row1[i]) - (row2[i]))**2\n",
+    "    return sqrt(distance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correct=0\n",
+    "total=0 \n",
+    "for j in range(45):\n",
+    "    row1=xtest[j].tolist() \n",
+    "    distances=[] \n",
+    "    for i in range(100):\n",
+    "        row2=xtrain[i].tolist() \n",
+    "        distance=euclidean_distance(row1,row2)\n",
+    "        distances.append((distance,ytrain[i]))\n",
+    "    votes=[i[1] for i in sorted(distances) [:5]] \n",
+    "    res=Counter(votes).most_common(1)[0][0]\n",
+    "    if(res==ytest[j]): \n",
+    "        correct+=1\n",
+    "    total+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(correct/total)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "USING SCIKIT-LEARN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
+    "classifier.fit(xtrain,ytrain)\n",
+    "prediction=classifier.predict(xtest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Metric 'minkowski' is used for measuring the euclidean distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
+      " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ytest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'\n",
+      " 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n",
+      " 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n",
+      " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
+      " 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'\n",
+      " 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
+      " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
+      " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
+      " 'Iris-virginica' 'Iris-virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "both the ytest and prediction give the same result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "cm = confusion_matrix(ytest, prediction)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16,  0,  0],\n",
+       "       [ 0, 19,  0],\n",
+       "       [ 0,  0, 15]], dtype=int64)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "confusion matrix gives the statistics of correct and incorrect predictions, hence, the there are no incorrect predictions in the matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}