diff --git a/tutorials/missing_data_imputation.ipynb b/tutorials/missing_data_imputation.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "86ee5293",
+   "metadata": {},
+   "source": [
+    "# Missing Data Imputation using Machine Learning Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11340512",
+   "metadata": {},
+   "source": [
+    "##  Introduction\n",
+    "\n",
+    "Missing data is a common problem in real-world datasets. Machine learning models cannot handle missing values directly, so we need to fill (impute) them.\n",
+    "\n",
+    "In this tutorial, we will explore different techniques to handle missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7c2e405",
+   "metadata": {},
+   "source": [
+    "##  Why Handle Missing Data?\n",
+    "\n",
+    "- Many ML algorithms cannot work with missing values\n",
+    "- Missing data can reduce model accuracy\n",
+    "- Proper imputation improves model performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f3541284",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.impute import SimpleImputer, KNNImputer\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a28d3e7f",
+   "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>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>50000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>55000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>65000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age   Salary\n",
+       "0  25.0  50000.0\n",
+       "1  30.0  60000.0\n",
+       "2   NaN  55000.0\n",
+       "3  35.0      NaN\n",
+       "4  40.0  65000.0\n",
+       "5   NaN  70000.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"Age\": [25, 30, np.nan, 35, 40, np.nan],\n",
+    "    \"Salary\": [50000, 60000, 55000, np.nan, 65000, 70000]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6d32952",
+   "metadata": {},
+   "source": [
+    "##  Dataset\n",
+    "\n",
+    "We created a small dataset with missing values to demonstrate imputation techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b68dc8b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age       2\n",
+       "Salary    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check missing values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa85cf3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.isnull().sum().plot(kind='bar')\n",
+    "plt.title(\"Missing Values Count\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04b33922",
+   "metadata": {},
+   "source": [
+    "# METHODS\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9fb73ca",
+   "metadata": {},
+   "source": [
+    "## 🔹 Mean Imputation\n",
+    "\n",
+    "Missing values are replaced with the mean of the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0e2077d4",
+   "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>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>50000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.5</td>\n",
+       "      <td>55000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>65000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>32.5</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age   Salary\n",
+       "0  25.0  50000.0\n",
+       "1  30.0  60000.0\n",
+       "2  32.5  55000.0\n",
+       "3  35.0  60000.0\n",
+       "4  40.0  65000.0\n",
+       "5  32.5  70000.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_imputer = SimpleImputer(strategy=\"mean\")\n",
+    "df_mean = pd.DataFrame(mean_imputer.fit_transform(df), columns=df.columns)\n",
+    "df_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "413b5891",
+   "metadata": {},
+   "source": [
+    "## 🔹 Median Imputation\n",
+    "\n",
+    "Missing values are replaced with the median of the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a7f0147e",
+   "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>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>50000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32.5</td>\n",
+       "      <td>55000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>65000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>32.5</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age   Salary\n",
+       "0  25.0  50000.0\n",
+       "1  30.0  60000.0\n",
+       "2  32.5  55000.0\n",
+       "3  35.0  60000.0\n",
+       "4  40.0  65000.0\n",
+       "5  32.5  70000.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_imputer = SimpleImputer(strategy=\"median\")\n",
+    "df_median = pd.DataFrame(median_imputer.fit_transform(df), columns=df.columns)\n",
+    "df_median"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d486e5f",
+   "metadata": {},
+   "source": [
+    "## 🔹 KNN Imputation\n",
+    "\n",
+    "KNN imputation fills missing values using nearest neighbors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cdea2819",
+   "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>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>50000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27.5</td>\n",
+       "      <td>55000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>62500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>65000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Age   Salary\n",
+       "0  25.0  50000.0\n",
+       "1  30.0  60000.0\n",
+       "2  27.5  55000.0\n",
+       "3  35.0  62500.0\n",
+       "4  40.0  65000.0\n",
+       "5  35.0  70000.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn_imputer = KNNImputer(n_neighbors=2)\n",
+    "df_knn = pd.DataFrame(knn_imputer.fit_transform(df), columns=df.columns)\n",
+    "df_knn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ab2260",
+   "metadata": {},
+   "source": [
+    "##  Comparison of Methods\n",
+    "\n",
+    "| Method  | Pros | Cons |\n",
+    "|--------|------|------|\n",
+    "| Mean   | Simple, fast | Sensitive to outliers |\n",
+    "| Median | Robust to outliers | May ignore distribution |\n",
+    "| KNN    | More accurate | Computationally expensive |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403b21fa",
+   "metadata": {},
+   "source": [
+    "##  Conclusion\n",
+    "\n",
+    "- Mean and median imputation are simple and quick methods\n",
+    "- KNN imputation provides better results but is slower\n",
+    "- The choice of method depends on the dataset and problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1384e70a",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}