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+{"cells":[{"cell_type":"markdown","metadata":{"id":"RHxCzcMw6yo1"},"source":["## What is K Nearest Neighbour ?"]},{"cell_type":"markdown","metadata":{"id":"itveBW6M69YO"},"source":["K-Nearest Neighbors (K-NN) is a supervised machine learning algorithm used for classification and regression tasks. It's a simple and intuitive algorithm that can be used for both classification and regression tasks."]},{"cell_type":"markdown","metadata":{"id":"NkX1uGxl7Q9N"},"source":["### Choosing K:\n","\n","One crucial hyperparameter in K-NN is the value of K. The choice of K can significantly impact the algorithm's performance. A smaller K value makes the algorithm more sensitive to noise in the data, potentially leading to overfitting, while a larger K value can make the decision boundary smoother but may result in underfitting."]},{"cell_type":"markdown","metadata":{"id":"d5jQMxtl86-A"},"source":["## When to use KNN ?"]},{"cell_type":"markdown","metadata":{"id":"Y8TXq9fS9DB4"},"source":["K-Nearest Neighbors (K-NN) is a versatile algorithm that can be useful in certain situations. Here are some scenarios in which you might consider using K-NN:\n","\n","* Multi-Class Classification: K-NN can also be applied to multi-class classification problems, where you need to classify data points into more than two classes. However, the choice of K and distance metric becomes more critical in these cases.\n","\n","* Regression: K-NN can be used for regression tasks when you need to predict a continuous target variable. It calculates the average (or weighted average) of the target values of the K nearest neighbors to make predictions.\n","\n","* Feature Engineering: K-NN can help identify important features in your dataset. By examining which features are most influential in determining the nearest neighbors, you can gain insights into the importance of various features."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":463},"id":"6QjqxoV0-K3Y","outputId":"8d55e697-19f2-4643-cf31-ed37c7e10b8f"},"outputs":[{"data":{"text/html":["<img src=\"https://miro.medium.com/v2/resize:fit:828/1*n9v1xsBi0bek98rqBnWGEg.gif\"/>"],"text/plain":["<IPython.core.display.Image object>"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["from IPython.display import Image\n","Image(url='https://miro.medium.com/v2/resize:fit:828/1*n9v1xsBi0bek98rqBnWGEg.gif')"]},{"cell_type":"markdown","metadata":{},"source":["## Real-life application of KNN:\n","\n","KNN can be used to predict customer churn in a business setting. For example, a company could use KNN to identify customers who are likely to cancel their subscription or stop using their product. The company could then target these customers with special offers or promotions in an attempt to retain them."]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","from sklearn.neighbors import KNeighborsClassifier\n","import matplotlib.pyplot as plt\n","\n","# Load and prepare the dataset\n","X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])\n","y = np.array([0, 0, 1, 1, 1])\n","\n","# Create the KNN model\n","knn = KNeighborsClassifier(n_neighbors=3)\n","\n","# Train the model\n","knn.fit(X, y)\n","\n","# Create a meshgrid for decision boundaries\n","x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n","y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n","xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))\n","\n","# Predict and plot the decision boundaries\n","Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n","Z = Z.reshape(xx.shape)\n","plt.contourf(xx, yy, Z, cmap='RdYlGn', alpha=0.4)\n","\n","# Plot the training data\n","plt.scatter(X[:, 0], X[:, 1], c=y, cmap='RdYlGn')\n","\n","# Label the axes\n","plt.xlabel('Feature 1')\n","plt.ylabel('Feature 2')\n","\n","# Show the plot\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["This code will create a scatter plot of the training data, with the different classes represented by different colors. The KNN decision boundaries will be overlaid on the plot as a shaded region."]},{"cell_type":"markdown","metadata":{},"source":["#### As you can see, the KNN model has learned to distinguish between the two classes of data points. The shaded region represents the area where the model predicts that a new data point is likely to belong to the red class.\n","\n","KNN is a simple but powerful machine learning algorithm that can be used for a variety of real-world tasks. It is particularly well-suited for classification problems where labeled data is scarce."]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.11.4"}},"nbformat":4,"nbformat_minor":0}
diff --git a/Computational Algorithms/Applications/Ml Algorithms/Iris.csv b/Computational Algorithms/Applications/Ml Algorithms/Iris.csv
deleted file mode 100644
index 79bc2c5..0000000
--- a/Computational Algorithms/Applications/Ml Algorithms/Iris.csv	
+++ /dev/null
@@ -1,151 +0,0 @@
-SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
-5.1,3.5,1.4,0.2,Iris-setosa
-4.9,3,1.4,0.2,Iris-setosa
-4.7,3.2,1.3,0.2,Iris-setosa
-4.6,3.1,1.5,0.2,Iris-setosa
-5,3.6,1.4,0.2,Iris-setosa
-5.4,3.9,1.7,0.4,Iris-setosa
-4.6,3.4,1.4,0.3,Iris-setosa
-5,3.4,1.5,0.2,Iris-setosa
-4.4,2.9,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-5.4,3.7,1.5,0.2,Iris-setosa
-4.8,3.4,1.6,0.2,Iris-setosa
-4.8,3,1.4,0.1,Iris-setosa
-4.3,3,1.1,0.1,Iris-setosa
-5.8,4,1.2,0.2,Iris-setosa
-5.7,4.4,1.5,0.4,Iris-setosa
-5.4,3.9,1.3,0.4,Iris-setosa
-5.1,3.5,1.4,0.3,Iris-setosa
-5.7,3.8,1.7,0.3,Iris-setosa
-5.1,3.8,1.5,0.3,Iris-setosa
-5.4,3.4,1.7,0.2,Iris-setosa
-5.1,3.7,1.5,0.4,Iris-setosa
-4.6,3.6,1,0.2,Iris-setosa
-5.1,3.3,1.7,0.5,Iris-setosa
-4.8,3.4,1.9,0.2,Iris-setosa
-5,3,1.6,0.2,Iris-setosa
-5,3.4,1.6,0.4,Iris-setosa
-5.2,3.5,1.5,0.2,Iris-setosa
-5.2,3.4,1.4,0.2,Iris-setosa
-4.7,3.2,1.6,0.2,Iris-setosa
-4.8,3.1,1.6,0.2,Iris-setosa
-5.4,3.4,1.5,0.4,Iris-setosa
-5.2,4.1,1.5,0.1,Iris-setosa
-5.5,4.2,1.4,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-5,3.2,1.2,0.2,Iris-setosa
-5.5,3.5,1.3,0.2,Iris-setosa
-4.9,3.1,1.5,0.1,Iris-setosa
-4.4,3,1.3,0.2,Iris-setosa
-5.1,3.4,1.5,0.2,Iris-setosa
-5,3.5,1.3,0.3,Iris-setosa
-4.5,2.3,1.3,0.3,Iris-setosa
-4.4,3.2,1.3,0.2,Iris-setosa
-5,3.5,1.6,0.6,Iris-setosa
-5.1,3.8,1.9,0.4,Iris-setosa
-4.8,3,1.4,0.3,Iris-setosa
-5.1,3.8,1.6,0.2,Iris-setosa
-4.6,3.2,1.4,0.2,Iris-setosa
-5.3,3.7,1.5,0.2,Iris-setosa
-5,3.3,1.4,0.2,Iris-setosa
-7,3.2,4.7,1.4,Iris-versicolor
-6.4,3.2,4.5,1.5,Iris-versicolor
-6.9,3.1,4.9,1.5,Iris-versicolor
-5.5,2.3,4,1.3,Iris-versicolor
-6.5,2.8,4.6,1.5,Iris-versicolor
-5.7,2.8,4.5,1.3,Iris-versicolor
-6.3,3.3,4.7,1.6,Iris-versicolor
-4.9,2.4,3.3,1,Iris-versicolor
-6.6,2.9,4.6,1.3,Iris-versicolor
-5.2,2.7,3.9,1.4,Iris-versicolor
-5,2,3.5,1,Iris-versicolor
-5.9,3,4.2,1.5,Iris-versicolor
-6,2.2,4,1,Iris-versicolor
-6.1,2.9,4.7,1.4,Iris-versicolor
-5.6,2.9,3.6,1.3,Iris-versicolor
-6.7,3.1,4.4,1.4,Iris-versicolor
-5.6,3,4.5,1.5,Iris-versicolor
-5.8,2.7,4.1,1,Iris-versicolor
-6.2,2.2,4.5,1.5,Iris-versicolor
-5.6,2.5,3.9,1.1,Iris-versicolor
-5.9,3.2,4.8,1.8,Iris-versicolor
-6.1,2.8,4,1.3,Iris-versicolor
-6.3,2.5,4.9,1.5,Iris-versicolor
-6.1,2.8,4.7,1.2,Iris-versicolor
-6.4,2.9,4.3,1.3,Iris-versicolor
-6.6,3,4.4,1.4,Iris-versicolor
-6.8,2.8,4.8,1.4,Iris-versicolor
-6.7,3,5,1.7,Iris-versicolor
-6,2.9,4.5,1.5,Iris-versicolor
-5.7,2.6,3.5,1,Iris-versicolor
-5.5,2.4,3.8,1.1,Iris-versicolor
-5.5,2.4,3.7,1,Iris-versicolor
-5.8,2.7,3.9,1.2,Iris-versicolor
-6,2.7,5.1,1.6,Iris-versicolor
-5.4,3,4.5,1.5,Iris-versicolor
-6,3.4,4.5,1.6,Iris-versicolor
-6.7,3.1,4.7,1.5,Iris-versicolor
-6.3,2.3,4.4,1.3,Iris-versicolor
-5.6,3,4.1,1.3,Iris-versicolor
-5.5,2.5,4,1.3,Iris-versicolor
-5.5,2.6,4.4,1.2,Iris-versicolor
-6.1,3,4.6,1.4,Iris-versicolor
-5.8,2.6,4,1.2,Iris-versicolor
-5,2.3,3.3,1,Iris-versicolor
-5.6,2.7,4.2,1.3,Iris-versicolor
-5.7,3,4.2,1.2,Iris-versicolor
-5.7,2.9,4.2,1.3,Iris-versicolor
-6.2,2.9,4.3,1.3,Iris-versicolor
-5.1,2.5,3,1.1,Iris-versicolor
-5.7,2.8,4.1,1.3,Iris-versicolor
-6.3,3.3,6,2.5,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-7.1,3,5.9,2.1,Iris-virginica
-6.3,2.9,5.6,1.8,Iris-virginica
-6.5,3,5.8,2.2,Iris-virginica
-7.6,3,6.6,2.1,Iris-virginica
-4.9,2.5,4.5,1.7,Iris-virginica
-7.3,2.9,6.3,1.8,Iris-virginica
-6.7,2.5,5.8,1.8,Iris-virginica
-7.2,3.6,6.1,2.5,Iris-virginica
-6.5,3.2,5.1,2,Iris-virginica
-6.4,2.7,5.3,1.9,Iris-virginica
-6.8,3,5.5,2.1,Iris-virginica
-5.7,2.5,5,2,Iris-virginica
-5.8,2.8,5.1,2.4,Iris-virginica
-6.4,3.2,5.3,2.3,Iris-virginica
-6.5,3,5.5,1.8,Iris-virginica
-7.7,3.8,6.7,2.2,Iris-virginica
-7.7,2.6,6.9,2.3,Iris-virginica
-6,2.2,5,1.5,Iris-virginica
-6.9,3.2,5.7,2.3,Iris-virginica
-5.6,2.8,4.9,2,Iris-virginica
-7.7,2.8,6.7,2,Iris-virginica
-6.3,2.7,4.9,1.8,Iris-virginica
-6.7,3.3,5.7,2.1,Iris-virginica
-7.2,3.2,6,1.8,Iris-virginica
-6.2,2.8,4.8,1.8,Iris-virginica
-6.1,3,4.9,1.8,Iris-virginica
-6.4,2.8,5.6,2.1,Iris-virginica
-7.2,3,5.8,1.6,Iris-virginica
-7.4,2.8,6.1,1.9,Iris-virginica
-7.9,3.8,6.4,2,Iris-virginica
-6.4,2.8,5.6,2.2,Iris-virginica
-6.3,2.8,5.1,1.5,Iris-virginica
-6.1,2.6,5.6,1.4,Iris-virginica
-7.7,3,6.1,2.3,Iris-virginica
-6.3,3.4,5.6,2.4,Iris-virginica
-6.4,3.1,5.5,1.8,Iris-virginica
-6,3,4.8,1.8,Iris-virginica
-6.9,3.1,5.4,2.1,Iris-virginica
-6.7,3.1,5.6,2.4,Iris-virginica
-6.9,3.1,5.1,2.3,Iris-virginica
-5.8,2.7,5.1,1.9,Iris-virginica
-6.8,3.2,5.9,2.3,Iris-virginica
-6.7,3.3,5.7,2.5,Iris-virginica
-6.7,3,5.2,2.3,Iris-virginica
-6.3,2.5,5,1.9,Iris-virginica
-6.5,3,5.2,2,Iris-virginica
-6.2,3.4,5.4,2.3,Iris-virginica
-5.9,3,5.1,1.8,Iris-virginica
diff --git a/Computational Algorithms/Applications/Ml Algorithms/PCA.ipynb b/Computational Algorithms/Applications/Ml Algorithms/PCA.ipynb
deleted file mode 100644
index bac740f..0000000
--- a/Computational Algorithms/Applications/Ml Algorithms/PCA.ipynb	
+++ /dev/null
@@ -1,620 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creating Taining Set\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv('Iris.csv')\n",
-    "original_data = df.iloc[:,:4]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "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>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>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>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>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>4.6</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>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>6.7</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6.3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>Iris-virginica</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>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>5.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
-       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
-       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
-       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
-       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
-       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
-       "..             ...           ...            ...           ...             ...\n",
-       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
-       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
-       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
-       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
-       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
-       "\n",
-       "[150 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step-1: Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "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>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>-0.900681</td>\n",
-       "      <td>1.032057</td>\n",
-       "      <td>-1.341272</td>\n",
-       "      <td>-1.312977</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1.143017</td>\n",
-       "      <td>-0.124958</td>\n",
-       "      <td>-1.341272</td>\n",
-       "      <td>-1.312977</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-1.385353</td>\n",
-       "      <td>0.337848</td>\n",
-       "      <td>-1.398138</td>\n",
-       "      <td>-1.312977</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-1.506521</td>\n",
-       "      <td>0.106445</td>\n",
-       "      <td>-1.284407</td>\n",
-       "      <td>-1.312977</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-1.021849</td>\n",
-       "      <td>1.263460</td>\n",
-       "      <td>-1.341272</td>\n",
-       "      <td>-1.312977</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
-       "0      -0.900681      1.032057      -1.341272     -1.312977  Iris-setosa\n",
-       "1      -1.143017     -0.124958      -1.341272     -1.312977  Iris-setosa\n",
-       "2      -1.385353      0.337848      -1.398138     -1.312977  Iris-setosa\n",
-       "3      -1.506521      0.106445      -1.284407     -1.312977  Iris-setosa\n",
-       "4      -1.021849      1.263460      -1.341272     -1.312977  Iris-setosa"
-      ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "df.iloc[:,0:4] = scaler.fit_transform(df.iloc[:,0:4])\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step-2: Covariance Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.00671141, -0.11010327,  0.87760486,  0.82344326],\n",
-       "       [-0.11010327,  1.00671141, -0.42333835, -0.358937  ],\n",
-       "       [ 0.87760486, -0.42333835,  1.00671141,  0.96921855],\n",
-       "       [ 0.82344326, -0.358937  ,  0.96921855,  1.00671141]])"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Cov_mat = np.cov( [ df.iloc[:,0],df.iloc[:,1],df.iloc[:,2],df.iloc[:,3] ] )\n",
-    "Cov_mat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step-3: Eigenval and Eigenvectors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eigenval,eigenvec = np.linalg.eig(Cov_mat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.93035378, 0.92740362, 0.14834223, 0.02074601])"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eigenval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.52237162, -0.37231836, -0.72101681,  0.26199559],\n",
-       "       [-0.26335492, -0.92555649,  0.24203288, -0.12413481],\n",
-       "       [ 0.58125401, -0.02109478,  0.14089226, -0.80115427],\n",
-       "       [ 0.56561105, -0.06541577,  0.6338014 ,  0.52354627]])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eigenvec"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## This is the real :: for ith row = ith eigenvalue"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.52237162, -0.26335492,  0.58125401,  0.56561105],\n",
-       "       [-0.37231836, -0.92555649, -0.02109478, -0.06541577],\n",
-       "       [-0.72101681,  0.24203288,  0.14089226,  0.6338014 ],\n",
-       "       [ 0.26199559, -0.12413481, -0.80115427,  0.52354627]])"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eigenvec.T "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get Largest Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.52237162, -0.26335492,  0.58125401,  0.56561105],\n",
-       "       [-0.37231836, -0.92555649, -0.02109478, -0.06541577]])"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "PC_1_2 = eigenvec.T[:2]\n",
-    "PC_1_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step-4: Transform Data into Reduced Dimension"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.52237162, -0.37231836],\n",
-       "       [-0.26335492, -0.92555649],\n",
-       "       [ 0.58125401, -0.02109478],\n",
-       "       [ 0.56561105, -0.06541577]])"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transpose_PC = PC_1_2.T\n",
-    "transpose_PC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "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>PC1</th>\n",
-       "      <th>PC2</th>\n",
-       "      <th>Species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2.669231</td>\n",
-       "      <td>-5.180887</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.696434</td>\n",
-       "      <td>-4.643645</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2.481163</td>\n",
-       "      <td>-4.752183</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2.571512</td>\n",
-       "      <td>-4.626615</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2.590658</td>\n",
-       "      <td>-5.236211</td>\n",
-       "      <td>Iris-setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>7.033251</td>\n",
-       "      <td>-5.531352</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6.613485</td>\n",
-       "      <td>-4.889261</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147</th>\n",
-       "      <td>6.759094</td>\n",
-       "      <td>-5.437263</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>6.782974</td>\n",
-       "      <td>-5.719634</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>6.274423</td>\n",
-       "      <td>-5.198680</td>\n",
-       "      <td>Iris-virginica</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>150 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          PC1       PC2         Species\n",
-       "0    2.669231 -5.180887     Iris-setosa\n",
-       "1    2.696434 -4.643645     Iris-setosa\n",
-       "2    2.481163 -4.752183     Iris-setosa\n",
-       "3    2.571512 -4.626615     Iris-setosa\n",
-       "4    2.590658 -5.236211     Iris-setosa\n",
-       "..        ...       ...             ...\n",
-       "145  7.033251 -5.531352  Iris-virginica\n",
-       "146  6.613485 -4.889261  Iris-virginica\n",
-       "147  6.759094 -5.437263  Iris-virginica\n",
-       "148  6.782974 -5.719634  Iris-virginica\n",
-       "149  6.274423 -5.198680  Iris-virginica\n",
-       "\n",
-       "[150 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Transformed = np.dot(original_data,transpose_PC)\n",
-    "\n",
-    "new_df = pd.DataFrame(Transformed,columns=['PC1','PC2'])\n",
-    "new_df['Species'] = df['Species']\n",
-    "new_df"
-   ]
-  },
-  {
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Computational Algorithms/Applications/DSA Algorithms/heapify.cpp b/Computational Algorithms/Heaps/heapify.cpp
similarity index 100%
rename from Computational Algorithms/Applications/DSA Algorithms/heapify.cpp
rename to Computational Algorithms/Heaps/heapify.cpp
diff --git a/Ml Algorithms/KNN/KNN.ipynb b/Ml Algorithms/KNN/KNN.ipynb
new file mode 100644
index 0000000..2388ddb
--- /dev/null
+++ b/Ml Algorithms/KNN/KNN.ipynb	
@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KNN \n",
+    "### KNN is a lazy runner: as without giving an input algo can't be runned "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "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>Age</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Purchased</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19</td>\n",
+       "      <td>19000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>35</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26</td>\n",
+       "      <td>43000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27</td>\n",
+       "      <td>57000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19</td>\n",
+       "      <td>76000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>46</td>\n",
+       "      <td>41000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>51</td>\n",
+       "      <td>23000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>50</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>36</td>\n",
+       "      <td>33000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>49</td>\n",
+       "      <td>36000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>400 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age  EstimatedSalary  Purchased\n",
+       "0     19            19000          0\n",
+       "1     35            20000          0\n",
+       "2     26            43000          0\n",
+       "3     27            57000          0\n",
+       "4     19            76000          0\n",
+       "..   ...              ...        ...\n",
+       "395   46            41000          1\n",
+       "396   51            23000          1\n",
+       "397   50            20000          1\n",
+       "398   36            33000          0\n",
+       "399   49            36000          1\n",
+       "\n",
+       "[400 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv('Social_Network_Ads.csv').iloc[:,2:]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scale = StandardScaler()\n",
+    "\n",
+    "df.iloc[:,:2] = scale.fit_transform(df.iloc[:,:2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "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>Age</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.797057</td>\n",
+       "      <td>-1.225763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>-0.253587</td>\n",
+       "      <td>0.536129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>0.797057</td>\n",
+       "      <td>0.359940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>0.319491</td>\n",
+       "      <td>-0.286087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>-0.444614</td>\n",
+       "      <td>2.327385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>1.561162</td>\n",
+       "      <td>1.005967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>348</th>\n",
+       "      <td>0.128465</td>\n",
+       "      <td>0.213115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>-0.158074</td>\n",
+       "      <td>1.417075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>-1.686284</td>\n",
+       "      <td>0.125021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>1.179110</td>\n",
+       "      <td>0.536129</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>280 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Age  EstimatedSalary\n",
+       "18   0.797057        -1.225763\n",
+       "83  -0.253587         0.536129\n",
+       "238  0.797057         0.359940\n",
+       "325  0.319491        -0.286087\n",
+       "103 -0.444614         2.327385\n",
+       "..        ...              ...\n",
+       "316  1.561162         1.005967\n",
+       "348  0.128465         0.213115\n",
+       "328 -0.158074         1.417075\n",
+       "149 -1.686284         0.125021\n",
+       "242  1.179110         0.536129\n",
+       "\n",
+       "[280 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "xtrain,xtest,ytrain,ytest = train_test_split(df.iloc[:,:2],df.iloc[:,-1],test_size=0.3)\n",
+    "xtrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-16 {color: black;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=1)"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.fit(xtrain,ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "85.83333333333333"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "ypred = knn.predict(xtest)\n",
+    "accuracy_score(ytest,ypred)*100"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Ml Algorithms/KNN/Social_Network_Ads.csv b/Ml Algorithms/KNN/Social_Network_Ads.csv
new file mode 100644
index 0000000..4a53849
--- /dev/null
+++ b/Ml Algorithms/KNN/Social_Network_Ads.csv	
@@ -0,0 +1,401 @@
+User ID,Gender,Age,EstimatedSalary,Purchased
+15624510,Male,19,19000,0
+15810944,Male,35,20000,0
+15668575,Female,26,43000,0
+15603246,Female,27,57000,0
+15804002,Male,19,76000,0
+15728773,Male,27,58000,0
+15598044,Female,27,84000,0
+15694829,Female,32,150000,1
+15600575,Male,25,33000,0
+15727311,Female,35,65000,0
+15570769,Female,26,80000,0
+15606274,Female,26,52000,0
+15746139,Male,20,86000,0
+15704987,Male,32,18000,0
+15628972,Male,18,82000,0
+15697686,Male,29,80000,0
+15733883,Male,47,25000,1
+15617482,Male,45,26000,1
+15704583,Male,46,28000,1
+15621083,Female,48,29000,1
+15649487,Male,45,22000,1
+15736760,Female,47,49000,1
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\ No newline at end of file