diff --git a/Wine quality prediction/README.md b/Wine quality prediction/README.md
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--- /dev/null
+++ b/Wine quality prediction/README.md	
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+### Wine Quality Prediction Using Machine Learning
+
+#### Project Overview:
+Welcome to the Wine Quality Prediction project, a fascinating initiative under the GirlScript Summer of Code program. This project focuses on employing machine learning techniques to predict the quality of wines based on their physicochemical properties. This project is designed to accommodate participants at various levels of expertise, guiding them through the intricacies of data handling, machine learning model building, and evaluation.
+
+#### Project Objectives:
+1. **Understanding Wine Quality Metrics**:
+   - Explore the various attributes that contribute to the quality of wine, such as acidity, sugar content, pH levels, and alcohol content.
+   - Learn about the standards and criteria used by experts to rate wine quality.
+
+2. **Data Preprocessing**:
+   - Clean and preprocess the dataset to handle missing values, outliers, and ensure the data is in a suitable format for analysis.
+   - Normalize and scale data to improve model performance.
+
+3. **Feature Engineering**:
+   - Create new features from the existing data to capture more complex relationships.
+   - Use techniques like one-hot encoding for categorical variables and polynomial features for nonlinear relationships.
+
+4. **Model Selection and Training**:
+   - Experiment with various machine learning algorithms including:
+     - **Linear Regression**: For a straightforward approach to prediction.
+     - **Decision Trees**: To understand decision-making paths.
+     - **Random Forests**: For handling overfitting and improving accuracy.
+     - **Support Vector Machines (SVM)**: For higher-dimensional space analysis.
+     - **Neural Networks**: For complex pattern recognition and prediction.
+
+5. **Model Evaluation**:
+   - Evaluate model performance using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score.
+   - Use cross-validation techniques to ensure robustness and reliability of the models.
+
+6. **Hyperparameter Tuning**:
+   - Optimize model parameters using Grid Search and Random Search methods to find the best combination of hyperparameters.
+   - Implement techniques like cross-validation and bootstrapping for better generalization.
+
+7. **Visualization**:
+   - Utilize data visualization libraries like Matplotlib and Seaborn to create insightful graphs and plots.
+   - Visualize feature importance, correlation matrices, and model performance metrics.
+
+#### Tech Stack:
+- **Programming Language**: Python
+- **Libraries**: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, TensorFlow/Keras (optional for neural networks)
+- **Tools**: Jupyter Notebook, Google Colab
+
+#### Learning Outcomes:
+By the end of this project, we will have:
+- A deep understanding of the principles and applications of machine learning.
+- Practical experience in handling and preprocessing real-world datasets.
+- The ability to build, evaluate, and tune machine learning models.
+- Enhanced skills in data visualization and interpretation of results.
+- The ability to communicate technical findings effectively.
diff --git a/Wine quality prediction/Wine_Quality_Prediction_using_Machine_Learning_with_Python.ipynb b/Wine quality prediction/Wine_Quality_Prediction_using_Machine_Learning_with_Python.ipynb
new file mode 100644
index 000000000..654a53d3e
--- /dev/null
+++ b/Wine quality prediction/Wine_Quality_Prediction_using_Machine_Learning_with_Python.ipynb	
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing the Dependencies"
+      ],
+      "metadata": {
+        "id": "lpmW9eyP8-jj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-mZu2H9i83b3"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import sklearn.datasets\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data Collection"
+      ],
+      "metadata": {
+        "id": "DhabZzAx15Tl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# loading the dataset to a Pandas Dataframe\n",
+        "wine_dataset = pd.read_csv('/content/winequality-red.csv')"
+      ],
+      "metadata": {
+        "id": "qqBaTL4V1hG1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# numebr of rows & columns in the dataset\n",
+        "wine_dataset.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Pmb9EgMK2ORg",
+        "outputId": "d990a15e-1e3e-4572-a484-3917c21a2c84"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1599, 12)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# first 5 rows of the dataset\n",
+        "wine_dataset.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "id": "nga_GTeG2UqS",
+        "outputId": "924f434c-a989-4683-bcf8-c9e3e2477a30"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+              "0            7.4              0.70         0.00             1.9      0.076   \n",
+              "1            7.8              0.88         0.00             2.6      0.098   \n",
+              "2            7.8              0.76         0.04             2.3      0.092   \n",
+              "3           11.2              0.28         0.56             1.9      0.075   \n",
+              "4            7.4              0.70         0.00             1.9      0.076   \n",
+              "\n",
+              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+              "\n",
+              "   alcohol  quality  \n",
+              "0      9.4        5  \n",
+              "1      9.8        5  \n",
+              "2      9.8        5  \n",
+              "3      9.8        6  \n",
+              "4      9.4        5  "
+            ],
+            "text/html": [
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.88</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.9968</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.76</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.9970</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.9980</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
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+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
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+              "    </div>\n",
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+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# checking for missing values\n",
+        "wine_dataset.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ccun2mY62b9g",
+        "outputId": "a3466528-018c-4d29-951c-ee7fc87bf182"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "fixed acidity           0\n",
+              "volatile acidity        0\n",
+              "citric acid             0\n",
+              "residual sugar          0\n",
+              "chlorides               0\n",
+              "free sulfur dioxide     0\n",
+              "total sulfur dioxide    0\n",
+              "density                 0\n",
+              "pH                      0\n",
+              "sulphates               0\n",
+              "alcohol                 0\n",
+              "quality                 0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data Analysis and Visualization"
+      ],
+      "metadata": {
+        "id": "J0zpC03y3BTr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# statistical measures of the dataset\n",
+        "wine_dataset.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        },
+        "id": "XqLlmONy2ygi",
+        "outputId": "fa2782a8-4fab-4f2c-b8b1-46e9711431a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+              "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
+              "mean        8.319637          0.527821     0.270976        2.538806   \n",
+              "std         1.741096          0.179060     0.194801        1.409928   \n",
+              "min         4.600000          0.120000     0.000000        0.900000   \n",
+              "25%         7.100000          0.390000     0.090000        1.900000   \n",
+              "50%         7.900000          0.520000     0.260000        2.200000   \n",
+              "75%         9.200000          0.640000     0.420000        2.600000   \n",
+              "max        15.900000          1.580000     1.000000       15.500000   \n",
+              "\n",
+              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+              "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
+              "mean      0.087467            15.874922             46.467792     0.996747   \n",
+              "std       0.047065            10.460157             32.895324     0.001887   \n",
+              "min       0.012000             1.000000              6.000000     0.990070   \n",
+              "25%       0.070000             7.000000             22.000000     0.995600   \n",
+              "50%       0.079000            14.000000             38.000000     0.996750   \n",
+              "75%       0.090000            21.000000             62.000000     0.997835   \n",
+              "max       0.611000            72.000000            289.000000     1.003690   \n",
+              "\n",
+              "                pH    sulphates      alcohol      quality  \n",
+              "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
+              "mean      3.311113     0.658149    10.422983     5.636023  \n",
+              "std       0.154386     0.169507     1.065668     0.807569  \n",
+              "min       2.740000     0.330000     8.400000     3.000000  \n",
+              "25%       3.210000     0.550000     9.500000     5.000000  \n",
+              "50%       3.310000     0.620000    10.200000     6.000000  \n",
+              "75%       3.400000     0.730000    11.100000     6.000000  \n",
+              "max       4.010000     2.000000    14.900000     8.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a4e0832e-bf26-405d-a363-63ef3d35f3a9\" class=\"colab-df-container\">\n",
+              "    <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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "      <td>1599.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>8.319637</td>\n",
+              "      <td>0.527821</td>\n",
+              "      <td>0.270976</td>\n",
+              "      <td>2.538806</td>\n",
+              "      <td>0.087467</td>\n",
+              "      <td>15.874922</td>\n",
+              "      <td>46.467792</td>\n",
+              "      <td>0.996747</td>\n",
+              "      <td>3.311113</td>\n",
+              "      <td>0.658149</td>\n",
+              "      <td>10.422983</td>\n",
+              "      <td>5.636023</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.741096</td>\n",
+              "      <td>0.179060</td>\n",
+              "      <td>0.194801</td>\n",
+              "      <td>1.409928</td>\n",
+              "      <td>0.047065</td>\n",
+              "      <td>10.460157</td>\n",
+              "      <td>32.895324</td>\n",
+              "      <td>0.001887</td>\n",
+              "      <td>0.154386</td>\n",
+              "      <td>0.169507</td>\n",
+              "      <td>1.065668</td>\n",
+              "      <td>0.807569</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>4.600000</td>\n",
+              "      <td>0.120000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.900000</td>\n",
+              "      <td>0.012000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>0.990070</td>\n",
+              "      <td>2.740000</td>\n",
+              "      <td>0.330000</td>\n",
+              "      <td>8.400000</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.100000</td>\n",
+              "      <td>0.390000</td>\n",
+              "      <td>0.090000</td>\n",
+              "      <td>1.900000</td>\n",
+              "      <td>0.070000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>22.000000</td>\n",
+              "      <td>0.995600</td>\n",
+              "      <td>3.210000</td>\n",
+              "      <td>0.550000</td>\n",
+              "      <td>9.500000</td>\n",
+              "      <td>5.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>7.900000</td>\n",
+              "      <td>0.520000</td>\n",
+              "      <td>0.260000</td>\n",
+              "      <td>2.200000</td>\n",
+              "      <td>0.079000</td>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>38.000000</td>\n",
+              "      <td>0.996750</td>\n",
+              "      <td>3.310000</td>\n",
+              "      <td>0.620000</td>\n",
+              "      <td>10.200000</td>\n",
+              "      <td>6.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>9.200000</td>\n",
+              "      <td>0.640000</td>\n",
+              "      <td>0.420000</td>\n",
+              "      <td>2.600000</td>\n",
+              "      <td>0.090000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>0.997835</td>\n",
+              "      <td>3.400000</td>\n",
+              "      <td>0.730000</td>\n",
+              "      <td>11.100000</td>\n",
+              "      <td>6.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>15.900000</td>\n",
+              "      <td>1.580000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>15.500000</td>\n",
+              "      <td>0.611000</td>\n",
+              "      <td>72.000000</td>\n",
+              "      <td>289.000000</td>\n",
+              "      <td>1.003690</td>\n",
+              "      <td>4.010000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>14.900000</td>\n",
+              "      <td>8.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4e0832e-bf26-405d-a363-63ef3d35f3a9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a4e0832e-bf26-405d-a363-63ef3d35f3a9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a4e0832e-bf26-405d-a363-63ef3d35f3a9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f2a8c625-a305-4e39-89b6-510af1677d6d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2a8c625-a305-4e39-89b6-510af1677d6d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f2a8c625-a305-4e39-89b6-510af1677d6d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# number of values for each quality\n",
+        "sns.catplot(x='quality', data = wine_dataset, kind = 'count')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "2HsvE2xu3K-h",
+        "outputId": "000b3cc8-997c-40ce-892a-852ab498e581"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.FacetGrid at 0x7e4159a90be0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# volatile acidity vs Quality\n",
+        "plot = plt.figure(figsize=(5,5))\n",
+        "sns.barplot(x='quality', y='volatile acidity', data = wine_dataset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 482
+        },
+        "id": "bEvBbHW231xu",
+        "outputId": "f9a59858-d84a-4f25-c2ac-7721c92b72d8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='quality', ylabel='volatile acidity'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# citric acid vs Quality\n",
+        "plot = plt.figure(figsize=(5,5))\n",
+        "sns.barplot(x='quality', y='citric acid', data = wine_dataset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "id": "2MXMNv_q4ht2",
+        "outputId": "4315fede-8eef-486c-cb5f-1a904c3227c8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='quality', ylabel='citric acid'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Correlation"
+      ],
+      "metadata": {
+        "id": "-BNYvh1G5JGK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Positive Correlation\n",
+        "\n",
+        "2. Negative Correlation"
+      ],
+      "metadata": {
+        "id": "NevD5ivJ5T44"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "correlation = wine_dataset.corr()"
+      ],
+      "metadata": {
+        "id": "vgwRQMdJ426E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# constructing a heatmap to undertsand the correlation between the columns\n",
+        "plt.figure(figsize=(10,10))\n",
+        "sns.heatmap(correlation, cbar=True, square=True, fmt = '.1f', annot = True, annot_kws={'size':8}, cmap = 'Blues')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 894
+        },
+        "id": "olWNqzPD5ci_",
+        "outputId": "adf34f1d-34be-4659-8eec-d7629e107cf5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data PreProcessing"
+      ],
+      "metadata": {
+        "id": "PCVx9nDb_LIW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# separate the data and Label\n",
+        "X = wine_dataset.drop('quality', axis=1)"
+      ],
+      "metadata": {
+        "id": "SkKLTCX46WcD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vQh3VDcP_bEB",
+        "outputId": "0c0a7a60-b8ae-43ae-dfaf-f1f8c25c5ab3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+            "0               7.4             0.700         0.00             1.9      0.076   \n",
+            "1               7.8             0.880         0.00             2.6      0.098   \n",
+            "2               7.8             0.760         0.04             2.3      0.092   \n",
+            "3              11.2             0.280         0.56             1.9      0.075   \n",
+            "4               7.4             0.700         0.00             1.9      0.076   \n",
+            "...             ...               ...          ...             ...        ...   \n",
+            "1594            6.2             0.600         0.08             2.0      0.090   \n",
+            "1595            5.9             0.550         0.10             2.2      0.062   \n",
+            "1596            6.3             0.510         0.13             2.3      0.076   \n",
+            "1597            5.9             0.645         0.12             2.0      0.075   \n",
+            "1598            6.0             0.310         0.47             3.6      0.067   \n",
+            "\n",
+            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+            "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
+            "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
+            "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
+            "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
+            "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
+            "...                   ...                   ...      ...   ...        ...   \n",
+            "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
+            "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
+            "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
+            "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
+            "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
+            "\n",
+            "      alcohol  \n",
+            "0         9.4  \n",
+            "1         9.8  \n",
+            "2         9.8  \n",
+            "3         9.8  \n",
+            "4         9.4  \n",
+            "...       ...  \n",
+            "1594     10.5  \n",
+            "1595     11.2  \n",
+            "1596     11.0  \n",
+            "1597     10.2  \n",
+            "1598     11.0  \n",
+            "\n",
+            "[1599 rows x 11 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Label Binarization"
+      ],
+      "metadata": {
+        "id": "MuUqhZuM_kW4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Y = wine_dataset['quality'].apply(lambda y_value: 1 if y_value>=7 else 0)"
+      ],
+      "metadata": {
+        "id": "Xo8Veukm_hyr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(Y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qyev6JldAMuO",
+        "outputId": "68954c4d-9de5-4318-aefc-bccfceebed5f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0       0\n",
+            "1       0\n",
+            "2       0\n",
+            "3       0\n",
+            "4       0\n",
+            "       ..\n",
+            "1594    0\n",
+            "1595    0\n",
+            "1596    0\n",
+            "1597    0\n",
+            "1598    0\n",
+            "Name: quality, Length: 1599, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Train & Test Split"
+      ],
+      "metadata": {
+        "id": "JkeAb1BZASR-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
+      ],
+      "metadata": {
+        "id": "VNP-10tMAOHg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(Y.shape, Y_train.shape, Y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ybvkUD-AAjE9",
+        "outputId": "66cee849-bfa7-4a11-a0b5-d8a2b0f0efa3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(1599,) (1279,) (320,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Model Training\n",
+        "\n",
+        "Random Forest Classifier"
+      ],
+      "metadata": {
+        "id": "jZDxASZtBCzh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = RandomForestClassifier()"
+      ],
+      "metadata": {
+        "id": "BkKNRtVoAotN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(X_train, Y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "48p-jF8ZBaiU",
+        "outputId": "fa84959d-5cf8-496e-8774-f9dcf2238def"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Model Evaluation"
+      ],
+      "metadata": {
+        "id": "ofhg216GBqF5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Accuracy Score"
+      ],
+      "metadata": {
+        "id": "Byb90N0oBuRo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# accuracy on test data\n",
+        "X_test_prediction = model.predict(X_test)\n",
+        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
+      ],
+      "metadata": {
+        "id": "L99bv0rcBeaO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Accuracy : ', test_data_accuracy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R0mbQ6lxCRDE",
+        "outputId": "dd0e9709-b716-49c7-caca-0344363e1953"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy :  0.909375\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Building a Predictive System"
+      ],
+      "metadata": {
+        "id": "02P-tPNuCmHq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "input_data = (7.3,0.65,0.0,1.2,0.065,15.0,21.0,0.9946,3.39,0.47,10.0)\n",
+        "\n",
+        "# changing the input data in a numpy array\n",
+        "input_data_as_numpy_array = np.asarray(input_data)\n",
+        "\n",
+        "# reshape the data as we are predicting the label for only one instance\n",
+        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
+        "\n",
+        "prediction = model.predict(input_data_reshaped)\n",
+        "print(prediction)\n",
+        "\n",
+        "if (prediction[0]==1):\n",
+        "  print('Good Quality Wine')\n",
+        "else:\n",
+        "  print('Bad Quality Wine')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rrjZtOtRCWMg",
+        "outputId": "a7ec41d1-d4c2-467a-9cf7-86fe875efa36"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1]\n",
+            "Good Quality Wine\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file