diff --git a/.github/workflows/majid_main.yml b/.github/workflows/majid_main.yml
new file mode 100644
index 0000000..9732799
--- /dev/null
+++ b/.github/workflows/majid_main.yml
@@ -0,0 +1,26 @@
+name: Assignment1
+
+on: 
+  push:
+    branches:
+      - Majid-dev
+
+permissions:
+  contents: read
+
+jobs:
+  run_tests:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+        
+      - name: Install Python
+        uses: actions/setup-python@v2
+              
+      - name: Install black
+        run: pip install black
+        
+      - name: Run black
+        run: python -m black --include "\\.py" ./
+        
diff --git a/machine_learning/KNNs.ipynb b/machine_learning/KNNs.ipynb
new file mode 100644
index 0000000..2e92140
--- /dev/null
+++ b/machine_learning/KNNs.ipynb
@@ -0,0 +1,969 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using KNN Algorithm to predict if a person will have diabetes or not"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import f1_score\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loading the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('diabetes.csv')"
+   ]
+  },
+  {
+   "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",
+       "    }\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>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "0            6      148             72             35        0  33.6   \n",
+       "1            1       85             66             29        0  26.6   \n",
+       "2            8      183             64              0        0  23.3   \n",
+       "3            1       89             66             23       94  28.1   \n",
+       "4            0      137             40             35      168  43.1   \n",
+       "\n",
+       "   DiabetesPedigreeFunction  Age  Outcome  \n",
+       "0                     0.627   50        1  \n",
+       "1                     0.351   31        0  \n",
+       "2                     0.672   32        1  \n",
+       "3                     0.167   21        0  \n",
+       "4                     2.288   33        1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "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>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>116</td>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>0.201</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "0            6      148             72             35        0  33.6   \n",
+       "1            1       85             66             29        0  26.6   \n",
+       "2            8      183             64              0        0  23.3   \n",
+       "3            1       89             66             23       94  28.1   \n",
+       "4            0      137             40             35      168  43.1   \n",
+       "5            5      116             74              0        0  25.6   \n",
+       "\n",
+       "   DiabetesPedigreeFunction  Age  Outcome  \n",
+       "0                     0.627   50        1  \n",
+       "1                     0.351   31        0  \n",
+       "2                     0.672   32        1  \n",
+       "3                     0.167   21        0  \n",
+       "4                     2.288   33        1  \n",
+       "5                     0.201   30        0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Replace columns like [Gluscose,BloodPressure,SkinThickness,BMI,Insulin] with Zero as values with mean of respective column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zero_not_accepted = ['Glucose','BloodPressure','SkinThickness','BMI','Insulin']\n",
+    "\n",
+    "for col in zero_not_accepted:\n",
+    "    data[col]= data[col].replace(0,np.NaN)\n",
+    "    mean = int(data[col].mean(skipna=True))\n",
+    "    data[col] = data[col].replace(np.NaN,mean)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### extracting independent variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data.iloc[:,0:8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### extracting dependent variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = data.iloc[:,8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explorning data to know relation before processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(data.corr())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### splitting dataset into training and testing set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>7</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>35.2</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>28.2</td>\n",
+       "      <td>0.443</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>680.0</td>\n",
+       "      <td>52.3</td>\n",
+       "      <td>0.427</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>1</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>8</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.183</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>10</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>180.0</td>\n",
+       "      <td>32.9</td>\n",
+       "      <td>0.171</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>7</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>30.4</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>4</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>24.7</td>\n",
+       "      <td>0.148</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>5</td>\n",
+       "      <td>136.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.640</td>\n",
+       "      <td>69</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>614 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "603            7    150.0           78.0           29.0    126.0  35.2   \n",
+       "118            4     97.0           60.0           23.0    155.0  28.2   \n",
+       "247            0    165.0           90.0           33.0    680.0  52.3   \n",
+       "157            1    109.0           56.0           21.0    135.0  25.2   \n",
+       "468            8    120.0           72.0           29.0    155.0  30.0   \n",
+       "..           ...      ...            ...            ...      ...   ...   \n",
+       "763           10    101.0           76.0           48.0    180.0  32.9   \n",
+       "192            7    159.0           66.0           29.0    155.0  30.4   \n",
+       "629            4     94.0           65.0           22.0    155.0  24.7   \n",
+       "559           11     85.0           74.0           29.0    155.0  30.1   \n",
+       "684            5    136.0           82.0           29.0    155.0  32.0   \n",
+       "\n",
+       "     DiabetesPedigreeFunction  Age  \n",
+       "603                     0.692   54  \n",
+       "118                     0.443   22  \n",
+       "247                     0.427   23  \n",
+       "157                     0.833   23  \n",
+       "468                     0.183   38  \n",
+       "..                        ...  ...  \n",
+       "763                     0.171   63  \n",
+       "192                     0.383   36  \n",
+       "629                     0.148   21  \n",
+       "559                     0.300   35  \n",
+       "684                     0.640   69  \n",
+       "\n",
+       "[614 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>1</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>42.9</td>\n",
+       "      <td>1.394</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>2</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.404</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>4</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.391</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>5</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>175.0</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>529</th>\n",
+       "      <td>0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>0.660</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>2</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>191.0</td>\n",
+       "      <td>33.7</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482</th>\n",
+       "      <td>4</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>27.8</td>\n",
+       "      <td>0.306</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>4</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>155.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0.645</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>3</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>26.3</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>1</td>\n",
+       "      <td>107.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>154 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "661            1    199.0           76.0           43.0    155.0  42.9   \n",
+       "122            2    107.0           74.0           30.0    100.0  33.6   \n",
+       "113            4     76.0           62.0           29.0    155.0  34.0   \n",
+       "14             5    166.0           72.0           19.0    175.0  25.8   \n",
+       "529            0    111.0           65.0           29.0    155.0  24.6   \n",
+       "..           ...      ...            ...            ...      ...   ...   \n",
+       "476            2    105.0           80.0           45.0    191.0  33.7   \n",
+       "482            4     85.0           58.0           22.0     49.0  27.8   \n",
+       "230            4    142.0           86.0           29.0    155.0  44.0   \n",
+       "527            3    116.0           74.0           15.0    105.0  26.3   \n",
+       "380            1    107.0           72.0           30.0     82.0  30.8   \n",
+       "\n",
+       "     DiabetesPedigreeFunction  Age  \n",
+       "661                     1.394   22  \n",
+       "122                     0.404   23  \n",
+       "113                     0.391   25  \n",
+       "14                      0.587   51  \n",
+       "529                     0.660   31  \n",
+       "..                        ...  ...  \n",
+       "476                     0.711   29  \n",
+       "482                     0.306   28  \n",
+       "230                     0.645   22  \n",
+       "527                     0.107   24  \n",
+       "380                     0.821   24  \n",
+       "\n",
+       "[154 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def Euclidian_Distance(test,train):\n",
+    "    distance=0\n",
+    "    for x in range(len(test)):\n",
+    "        distance += pow((test[x] - train[x]), 2)\n",
+    "    return math.sqrt(distance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def KNNClassifier(K):\n",
+    "    labels=[]\n",
+    "    num_test = X_test.shape[0]\n",
+    "    num_train = X_train.shape[0]\n",
+    "    for i in range(num_test):\n",
+    "        distance=[] \n",
+    "        index=[]\n",
+    "        for j in range(num_train):\n",
+    "            distance.append(Euclidian_Distance(X_test.iloc[i],X_train.iloc[j]))\n",
+    "            index.append(j)\n",
+    "        #dis_list = zip(distances, y_train)\n",
+    "        #sorted_list = sorted(dis_list)\n",
+    "        df1=pd.DataFrame({'Dis':distance,'ind':index})\n",
+    "        df1=df1.sort_values('Dis')\n",
+    "        zero_count = 0\n",
+    "        one_count = 0\n",
+    "        for i in df1['ind'].head(K):\n",
+    "            if y_train.iloc[i] == 1:\n",
+    "                one_count += 1\n",
+    "            else:\n",
+    "                zero_count += 1\n",
+    "        if one_count >= zero_count:\n",
+    "            labels.append(1)\n",
+    "        else:\n",
+    "            labels.append(0)\n",
+    "    return labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels=KNNClassifier(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "154"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test = np.array(y_test)\n",
+    "labels=np.array(labels)\n",
+    "labels.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "72.72727272727273 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test = list(y_test)\n",
+    "tcount = 0\n",
+    "for i in range(len(labels)):\n",
+    "    if labels[i] == y_test[i]:\n",
+    "        tcount += 1\n",
+    "print((tcount / len(y_test))*100,\"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/machine_learning/KNNs.py b/machine_learning/KNNs.py
new file mode 100644
index 0000000..512c1b2
--- /dev/null
+++ b/machine_learning/KNNs.py
@@ -0,0 +1,178 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Using KNN Algorithm to predict if a person will have diabetes or not
+
+# ### importing libraries
+
+# In[1]:
+
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import f1_score
+from sklearn.metrics import accuracy_score
+import seaborn as sns
+import matplotlib.pyplot as plt
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+
+# ### loading the dataset
+
+# In[2]:
+
+
+data = pd.read_csv('diabetes.csv')
+
+
+# In[3]:
+
+
+data.head()
+
+
+# In[4]:
+
+
+data[0:6]
+
+
+# #### Replace columns like [Gluscose,BloodPressure,SkinThickness,BMI,Insulin] with Zero as values with mean of respective column
+
+# In[5]:
+
+
+zero_not_accepted = ['Glucose','BloodPressure','SkinThickness','BMI','Insulin']
+
+for col in zero_not_accepted:
+    data[col]= data[col].replace(0,np.NaN)
+    mean = int(data[col].mean(skipna=True))
+    data[col] = data[col].replace(np.NaN,mean)
+
+
+# ### extracting independent variables
+
+# In[6]:
+
+
+X = data.iloc[:,0:8]
+
+
+# ### extracting dependent variable
+
+# In[7]:
+
+
+y = data.iloc[:,8]
+
+
+# ### Explorning data to know relation before processing
+
+# In[8]:
+
+
+sns.heatmap(data.corr())
+
+
+# ### splitting dataset into training and testing set
+
+# In[9]:
+
+
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
+
+
+# In[10]:
+
+
+X_train
+
+
+# In[11]:
+
+
+X_test
+
+
+# In[12]:
+
+
+import math
+def Euclidian_Distance(test,train):
+    distance=0
+    for x in range(len(test)):
+        distance += pow((test[x] - train[x]), 2)
+    return math.sqrt(distance)
+
+
+# In[13]:
+
+
+def KNNClassifier(K):
+    labels=[]
+    num_test = X_test.shape[0]
+    num_train = X_train.shape[0]
+    for i in range(num_test):
+        distance=[] 
+        index=[]
+        for j in range(num_train):
+            distance.append(Euclidian_Distance(X_test.iloc[i],X_train.iloc[j]))
+            index.append(j)
+        #dis_list = zip(distances, y_train)
+        #sorted_list = sorted(dis_list)
+        df1=pd.DataFrame({'Dis':distance,'ind':index})
+        df1=df1.sort_values('Dis')
+        zero_count = 0
+        one_count = 0
+        for i in df1['ind'].head(K):
+            if y_train.iloc[i] == 1:
+                one_count += 1
+            else:
+                zero_count += 1
+        if one_count >= zero_count:
+            labels.append(1)
+        else:
+            labels.append(0)
+    return labels
+
+
+# In[14]:
+
+
+labels=KNNClassifier(3)
+
+
+# In[ ]:
+
+
+
+
+
+# In[16]:
+
+
+y_test = np.array(y_test)
+labels=np.array(labels)
+labels.shape[0]
+
+
+# In[18]:
+
+
+y_test = list(y_test)
+tcount = 0
+for i in range(len(labels)):
+    if labels[i] == y_test[i]:
+        tcount += 1
+print((tcount / len(y_test))*100,"%")
+
+
+# In[ ]:
+
+
+
+
diff --git a/machine_learning/diabetes.csv b/machine_learning/diabetes.csv
new file mode 100644
index 0000000..dcaf5fe
--- /dev/null
+++ b/machine_learning/diabetes.csv
@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
+7,150,66,42,342,34.7,0.718,42,0
+1,73,50,10,0,23,0.248,21,0
+7,187,68,39,304,37.7,0.254,41,1
+0,100,88,60,110,46.8,0.962,31,0
+0,146,82,0,0,40.5,1.781,44,0
+0,105,64,41,142,41.5,0.173,22,0
+2,84,0,0,0,0,0.304,21,0
+8,133,72,0,0,32.9,0.27,39,1
+5,44,62,0,0,25,0.587,36,0
+2,141,58,34,128,25.4,0.699,24,0
+7,114,66,0,0,32.8,0.258,42,1
+5,99,74,27,0,29,0.203,32,0
+0,109,88,30,0,32.5,0.855,38,1
+2,109,92,0,0,42.7,0.845,54,0
+1,95,66,13,38,19.6,0.334,25,0
+4,146,85,27,100,28.9,0.189,27,0
+2,100,66,20,90,32.9,0.867,28,1
+5,139,64,35,140,28.6,0.411,26,0
+13,126,90,0,0,43.4,0.583,42,1
+4,129,86,20,270,35.1,0.231,23,0
+1,79,75,30,0,32,0.396,22,0
+1,0,48,20,0,24.7,0.14,22,0
+7,62,78,0,0,32.6,0.391,41,0
+5,95,72,33,0,37.7,0.37,27,0
+0,131,0,0,0,43.2,0.27,26,1
+2,112,66,22,0,25,0.307,24,0
+3,113,44,13,0,22.4,0.14,22,0
+2,74,0,0,0,0,0.102,22,0
+7,83,78,26,71,29.3,0.767,36,0
+0,101,65,28,0,24.6,0.237,22,0
+5,137,108,0,0,48.8,0.227,37,1
+2,110,74,29,125,32.4,0.698,27,0
+13,106,72,54,0,36.6,0.178,45,0
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