ml-model-overfitting-the-data.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/sridharstreaks/ml-model-overfitting-the-data?scriptVersionId=121540398\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"c41b3d32","metadata":{"papermill":{"duration":0.00828,"end_time":"2023-03-09T09:17:33.553752","exception":false,"start_time":"2023-03-09T09:17:33.545472","status":"completed"},"tags":[]},"source":["# EDA and Creating model to Predicts Maths Score.\n","\n","#### In this notebook i tried to create a Ml model that predicts the \"Maths Score\" using various parameters.\n","\n","**I tried using linear regression and random forest but those end up overfitting results. I'll futher dicuss why that happened and discuss measures to mitigate the issues at the end of the notebook**\n","\n","#### Lets's Begin"]},{"cell_type":"markdown","id":"7a8eee7e","metadata":{"papermill":{"duration":0.00749,"end_time":"2023-03-09T09:17:33.56843","exception":false,"start_time":"2023-03-09T09:17:33.56094","status":"completed"},"tags":[]},"source":["# Loading Libraries and Dataset"]},{"cell_type":"code","execution_count":1,"id":"6971f7d3","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.584852Z","iopub.status.busy":"2023-03-09T09:17:33.584432Z","iopub.status.idle":"2023-03-09T09:17:33.707595Z","shell.execute_reply":"2023-03-09T09:17:33.706069Z"},"papermill":{"duration":0.134974,"end_time":"2023-03-09T09:17:33.710561","exception":false,"start_time":"2023-03-09T09:17:33.575587","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["        <script type=\"text/javascript\">\n","        window.PlotlyConfig = {MathJaxConfig: 'local'};\n","        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n","        if (typeof require !== 'undefined') {\n","        require.undef(\"plotly\");\n","        requirejs.config({\n","            paths: {\n","                'plotly': ['https://cdn.plot.ly/plotly-2.18.0.min']\n","            }\n","        });\n","        require(['plotly'], function(Plotly) {\n","            window._Plotly = Plotly;\n","        });\n","        }\n","        </script>\n","        "]},"metadata":{},"output_type":"display_data"}],"source":["import pandas as pd\n","from plotly.offline import init_notebook_mode, iplot\n","init_notebook_mode(connected=True)"]},{"cell_type":"code","execution_count":2,"id":"eeffbdf2","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.727776Z","iopub.status.busy":"2023-03-09T09:17:33.727287Z","iopub.status.idle":"2023-03-09T09:17:33.750169Z","shell.execute_reply":"2023-03-09T09:17:33.748759Z"},"papermill":{"duration":0.035128,"end_time":"2023-03-09T09:17:33.753204","exception":false,"start_time":"2023-03-09T09:17:33.718076","status":"completed"},"tags":[]},"outputs":[],"source":["df=pd.read_csv(\"/kaggle/input/student-performance-in-mathematics/Student performance in mathematics.csv\")"]},{"cell_type":"code","execution_count":3,"id":"de16e65c","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.77152Z","iopub.status.busy":"2023-03-09T09:17:33.770156Z","iopub.status.idle":"2023-03-09T09:17:33.807746Z","shell.execute_reply":"2023-03-09T09:17:33.806095Z"},"papermill":{"duration":0.05024,"end_time":"2023-03-09T09:17:33.810834","exception":false,"start_time":"2023-03-09T09:17:33.760594","status":"completed"},"tags":[]},"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>Student ID</th>\n","      <th>Gender</th>\n","      <th>Race/ethnicity</th>\n","      <th>Parental level of education</th>\n","      <th>Lunch</th>\n","      <th>Test preparation course</th>\n","      <th>Math score</th>\n","      <th>Reading score</th>\n","      <th>Writing score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Female</td>\n","      <td>Group B</td>\n","      <td>Bachelor's degree</td>\n","      <td>Yes</td>\n","      <td>Completed</td>\n","      <td>72</td>\n","      <td>72</td>\n","      <td>74</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Female</td>\n","      <td>Group C</td>\n","      <td>Some college</td>\n","      <td>No</td>\n","      <td>None</td>\n","      <td>69</td>\n","      <td>90</td>\n","      <td>88</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Female</td>\n","      <td>Group B</td>\n","      <td>Master's degree</td>\n","      <td>No</td>\n","      <td>Completed</td>\n","      <td>90</td>\n","      <td>95</td>\n","      <td>93</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Male</td>\n","      <td>Group A</td>\n","      <td>Associate's degree</td>\n","      <td>Yes</td>\n","      <td>None</td>\n","      <td>47</td>\n","      <td>57</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Male</td>\n","      <td>Group C</td>\n","      <td>Some college</td>\n","      <td>Yes</td>\n","      <td>None</td>\n","      <td>76</td>\n","      <td>78</td>\n","      <td>75</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>Female</td>\n","      <td>Group B</td>\n","      <td>Associate's degree</td>\n","      <td>No</td>\n","      <td>Completed</td>\n","      <td>71</td>\n","      <td>83</td>\n","      <td>78</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>Female</td>\n","      <td>Group B</td>\n","      <td>Some college</td>\n","      <td>No</td>\n","      <td>None</td>\n","      <td>88</td>\n","      <td>95</td>\n","      <td>92</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>Male</td>\n","      <td>Group B</td>\n","      <td>Some college</td>\n","      <td>Yes</td>\n","      <td>None</td>\n","      <td>40</td>\n","      <td>43</td>\n","      <td>39</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>Male</td>\n","      <td>Group D</td>\n","      <td>High school</td>\n","      <td>Yes</td>\n","      <td>Completed</td>\n","      <td>64</td>\n","      <td>64</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>Female</td>\n","      <td>Group B</td>\n","      <td>High school</td>\n","      <td>Yes</td>\n","      <td>None</td>\n","      <td>38</td>\n","      <td>60</td>\n","      <td>50</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Student ID  Gender Race/ethnicity Parental level of education Lunch  \\\n","0           1  Female        Group B           Bachelor's degree   Yes   \n","1           2  Female        Group C                Some college    No   \n","2           3  Female        Group B             Master's degree    No   \n","3           4    Male        Group A          Associate's degree   Yes   \n","4           5    Male        Group C                Some college   Yes   \n","5           6  Female        Group B          Associate's degree    No   \n","6           7  Female        Group B                Some college    No   \n","7           8    Male        Group B                Some college   Yes   \n","8           9    Male        Group D                 High school   Yes   \n","9          10  Female        Group B                 High school   Yes   \n","\n","  Test preparation course  Math score  Reading score  Writing score  \n","0               Completed          72             72             74  \n","1                    None          69             90             88  \n","2               Completed          90             95             93  \n","3                    None          47             57             44  \n","4                    None          76             78             75  \n","5               Completed          71             83             78  \n","6                    None          88             95             92  \n","7                    None          40             43             39  \n","8               Completed          64             64             67  \n","9                    None          38             60             50  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.head(10)"]},{"cell_type":"markdown","id":"80147149","metadata":{"papermill":{"duration":0.007544,"end_time":"2023-03-09T09:17:33.826283","exception":false,"start_time":"2023-03-09T09:17:33.818739","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"e344b8db","metadata":{"papermill":{"duration":0.007653,"end_time":"2023-03-09T09:17:33.842332","exception":false,"start_time":"2023-03-09T09:17:33.834679","status":"completed"},"tags":[]},"source":["# Overviewing the data"]},{"cell_type":"code","execution_count":4,"id":"9a875bbc","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.860986Z","iopub.status.busy":"2023-03-09T09:17:33.860576Z","iopub.status.idle":"2023-03-09T09:17:33.887282Z","shell.execute_reply":"2023-03-09T09:17:33.885732Z"},"papermill":{"duration":0.039862,"end_time":"2023-03-09T09:17:33.89005","exception":false,"start_time":"2023-03-09T09:17:33.850188","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 207 entries, 0 to 206\n","Data columns (total 9 columns):\n"," #   Column                       Non-Null Count  Dtype \n","---  ------                       --------------  ----- \n"," 0   Student ID                   207 non-null    int64 \n"," 1   Gender                       207 non-null    object\n"," 2   Race/ethnicity               207 non-null    object\n"," 3   Parental level of education  207 non-null    object\n"," 4   Lunch                        207 non-null    object\n"," 5   Test preparation course      207 non-null    object\n"," 6   Math score                   207 non-null    int64 \n"," 7   Reading score                207 non-null    int64 \n"," 8   Writing score                207 non-null    int64 \n","dtypes: int64(4), object(5)\n","memory usage: 14.7+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":5,"id":"0077c030","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.908196Z","iopub.status.busy":"2023-03-09T09:17:33.907777Z","iopub.status.idle":"2023-03-09T09:17:33.936459Z","shell.execute_reply":"2023-03-09T09:17:33.935076Z"},"papermill":{"duration":0.041512,"end_time":"2023-03-09T09:17:33.939663","exception":false,"start_time":"2023-03-09T09:17:33.898151","status":"completed"},"tags":[]},"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>Student ID</th>\n","      <th>Math score</th>\n","      <th>Reading score</th>\n","      <th>Writing score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>207.000000</td>\n","      <td>207.000000</td>\n","      <td>207.000000</td>\n","      <td>207.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>104.000000</td>\n","      <td>69.207729</td>\n","      <td>69.898551</td>\n","      <td>68.971014</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>59.899917</td>\n","      <td>13.528838</td>\n","      <td>14.960607</td>\n","      <td>15.843259</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>38.000000</td>\n","      <td>38.000000</td>\n","      <td>35.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>52.500000</td>\n","      <td>59.500000</td>\n","      <td>59.000000</td>\n","      <td>57.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>104.000000</td>\n","      <td>69.000000</td>\n","      <td>70.000000</td>\n","      <td>70.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>155.500000</td>\n","      <td>79.000000</td>\n","      <td>80.000000</td>\n","      <td>80.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>207.000000</td>\n","      <td>98.000000</td>\n","      <td>100.000000</td>\n","      <td>100.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Student ID  Math score  Reading score  Writing score\n","count  207.000000  207.000000     207.000000     207.000000\n","mean   104.000000   69.207729      69.898551      68.971014\n","std     59.899917   13.528838      14.960607      15.843259\n","min      1.000000   38.000000      38.000000      35.000000\n","25%     52.500000   59.500000      59.000000      57.000000\n","50%    104.000000   69.000000      70.000000      70.000000\n","75%    155.500000   79.000000      80.000000      80.000000\n","max    207.000000   98.000000     100.000000     100.000000"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"code","execution_count":6,"id":"1bc32a55","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:33.957827Z","iopub.status.busy":"2023-03-09T09:17:33.957424Z","iopub.status.idle":"2023-03-09T09:17:33.968153Z","shell.execute_reply":"2023-03-09T09:17:33.966848Z"},"papermill":{"duration":0.023045,"end_time":"2023-03-09T09:17:33.970889","exception":false,"start_time":"2023-03-09T09:17:33.947844","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Student ID                     0\n","Gender                         0\n","Race/ethnicity                 0\n","Parental level of education    0\n","Lunch                          0\n","Test preparation course        0\n","Math score                     0\n","Reading score                  0\n","Writing score                  0\n","dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.isna().sum()"]},{"cell_type":"markdown","id":"dc6b4b0d","metadata":{"papermill":{"duration":0.007923,"end_time":"2023-03-09T09:17:33.987415","exception":false,"start_time":"2023-03-09T09:17:33.979492","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"5f69f77a","metadata":{"papermill":{"duration":0.008166,"end_time":"2023-03-09T09:17:34.004481","exception":false,"start_time":"2023-03-09T09:17:33.996315","status":"completed"},"tags":[]},"source":["# EDA Through Plots"]},{"cell_type":"code","execution_count":7,"id":"b519f944","metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2023-03-09T09:17:34.023323Z","iopub.status.busy":"2023-03-09T09:17:34.022867Z","iopub.status.idle":"2023-03-09T09:17:38.000761Z","shell.execute_reply":"2023-03-09T09:17:37.99906Z"},"papermill":{"duration":3.990905,"end_time":"2023-03-09T09:17:38.003872","exception":false,"start_time":"2023-03-09T09:17:34.012967","status":"completed"},"tags":[]},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"Gender=Female<br>Math score=%{x}<br>Reading 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preparation course\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Reading score\"}},\"legend\":{\"title\":{\"text\":\"Parental level of education\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Test preparation course vs Reading score\"},\"violinmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n","                            \n","var gd = document.getElementById('1ed5b741-5dd0-464c-bcb5-64a4c828c59a');\n","var x = new MutationObserver(function (mutations, observer) {{\n","        var display = window.getComputedStyle(gd).display;\n","        if (!display || display === 'none') {{\n","            console.log([gd, 'removed!']);\n","            Plotly.purge(gd);\n","            observer.disconnect();\n","        }}\n","}});\n","\n","// Listen for the removal of the full notebook cells\n","var notebookContainer = gd.closest('#notebook-container');\n","if (notebookContainer) {{\n","    x.observe(notebookContainer, {childList: true});\n","}}\n","\n","// Listen for the clearing of the current output cell\n","var outputEl = gd.closest('.output');\n","if (outputEl) {{\n","    x.observe(outputEl, {childList: true});\n","}}\n","\n","                        })                };                });            </script>        </div>"]},"metadata":{},"output_type":"display_data"}],"source":["fig = px.violin(df, x='Test preparation course', y='Reading score', \n","                color='Parental level of education', box=True, points='all',\n","                title='Test preparation course vs Reading score')\n","fig.show()"]},{"cell_type":"markdown","id":"7944a8d9","metadata":{"papermill":{"duration":0.050959,"end_time":"2023-03-09T09:17:39.033601","exception":false,"start_time":"2023-03-09T09:17:38.982642","status":"completed"},"tags":[]},"source":["--------------------------------------------------------------------------------------------------------"]},{"cell_type":"markdown","id":"c034d076","metadata":{"papermill":{"duration":0.050395,"end_time":"2023-03-09T09:17:39.132748","exception":false,"start_time":"2023-03-09T09:17:39.082353","status":"completed"},"tags":[]},"source":["# Data preprocessing For ML Modelling\n","\n","**I'm using Lablel Encoder to change catogory columns to numeric equalent for regression**"]},{"cell_type":"code","execution_count":13,"id":"cb90b56e","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:39.235013Z","iopub.status.busy":"2023-03-09T09:17:39.2342Z","iopub.status.idle":"2023-03-09T09:17:40.176533Z","shell.execute_reply":"2023-03-09T09:17:40.175287Z"},"papermill":{"duration":0.997936,"end_time":"2023-03-09T09:17:40.179753","exception":false,"start_time":"2023-03-09T09:17:39.181817","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.preprocessing import LabelEncoder, StandardScaler\n","\n","# Preprocess the data\n","le = LabelEncoder()\n","df['Gender'] = le.fit_transform(df['Gender'])\n","df['Race/ethnicity'] = le.fit_transform(df['Race/ethnicity'])\n","df['Parental level of education'] = le.fit_transform(df['Parental level of education'])\n","df['Lunch'] = le.fit_transform(df['Lunch'])\n","df['Test preparation course'] = le.fit_transform(df['Test preparation course'])\n","\n","scaler = StandardScaler()\n","df[['Reading score', 'Writing score']] = scaler.fit_transform(df[['Reading score', 'Writing score']])"]},{"cell_type":"code","execution_count":14,"id":"730d76f9","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:40.278239Z","iopub.status.busy":"2023-03-09T09:17:40.277389Z","iopub.status.idle":"2023-03-09T09:17:40.36385Z","shell.execute_reply":"2023-03-09T09:17:40.362513Z"},"papermill":{"duration":0.139493,"end_time":"2023-03-09T09:17:40.366896","exception":false,"start_time":"2023-03-09T09:17:40.227403","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","# Split the data into training and testing sets\n","X = df.drop(['Math score', 'Reading score', 'Writing score'], axis=1)\n","y = df['Math score']\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","id":"446e616f","metadata":{"papermill":{"duration":0.04677,"end_time":"2023-03-09T09:17:40.460476","exception":false,"start_time":"2023-03-09T09:17:40.413706","status":"completed"},"tags":[]},"source":["# Implementing Regression Models on Pre-Processed Data"]},{"cell_type":"code","execution_count":15,"id":"1a7b1998","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:40.55984Z","iopub.status.busy":"2023-03-09T09:17:40.559085Z","iopub.status.idle":"2023-03-09T09:17:40.697978Z","shell.execute_reply":"2023-03-09T09:17:40.696194Z"},"papermill":{"duration":0.193291,"end_time":"2023-03-09T09:17:40.700967","exception":false,"start_time":"2023-03-09T09:17:40.507676","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Training score: 0.3832909552981765\n","Testing score: 0.5121263478667983\n"]}],"source":["from sklearn.linear_model import LinearRegression\n","\n","# Train a linear regression model\n","lr = LinearRegression()\n","lr.fit(X_train, y_train)\n","\n","# Evaluate the model\n","train_score = lr.score(X_train, y_train)\n","test_score = lr.score(X_test, y_test)\n","\n","print('Training score:', train_score)\n","print('Testing score:', test_score)"]},{"cell_type":"markdown","id":"37dd5564","metadata":{"papermill":{"duration":0.047648,"end_time":"2023-03-09T09:17:40.797053","exception":false,"start_time":"2023-03-09T09:17:40.749405","status":"completed"},"tags":[]},"source":["**The model has a training score of 0.383 and a testing score of 0.512. This suggests that the model may be overfitting to the training data, and may not generalize well to new data. we may want to try using a more complex model, or using a regularization technique such as Lasso or Ridge Regression to improve the performance of the model. Additionally, we can also try to engineer new features or use feature selection techniques to improve the model's performance.**\n","\n","**So, I tried the slightly coplex Random Forest Regressor**"]},{"cell_type":"code","execution_count":16,"id":"6bdd1975","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:40.89555Z","iopub.status.busy":"2023-03-09T09:17:40.894019Z","iopub.status.idle":"2023-03-09T09:17:41.391939Z","shell.execute_reply":"2023-03-09T09:17:41.390475Z"},"papermill":{"duration":0.551363,"end_time":"2023-03-09T09:17:41.396275","exception":false,"start_time":"2023-03-09T09:17:40.844912","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Training score: 0.8951748547727831\n","Testing score: 0.36044122008952106\n"]}],"source":["from sklearn.ensemble import RandomForestRegressor\n","from sklearn.metrics import r2_score\n","\n","# Train a Random Forest Regressor\n","rf = RandomForestRegressor(n_estimators=100, random_state=42)\n","rf.fit(X_train, y_train)\n","\n","# Evaluate the model\n","y_train_pred = rf.predict(X_train)\n","y_test_pred = rf.predict(X_test)\n","\n","train_score = r2_score(y_train, y_train_pred)\n","test_score = r2_score(y_test, y_test_pred)\n","\n","print('Training score:', train_score)\n","print('Testing score:', test_score)"]},{"cell_type":"markdown","id":"35991749","metadata":{"papermill":{"duration":0.055726,"end_time":"2023-03-09T09:17:41.501584","exception":false,"start_time":"2023-03-09T09:17:41.445858","status":"completed"},"tags":[]},"source":["**The model has a training score of 0.895 and a testing score of 0.360. Again, This suggests that the model is overfitting to the training data, and is not able to generalize well to new data. One possible reason for this could be that the model is too complex, and is fitting the noise in the training data.**\n","\n","**So, This time i tried using other ensemble methods such as Gradient Boosting or XGBoost to improve the model's performance.**"]},{"cell_type":"code","execution_count":17,"id":"a2700be2","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:41.605629Z","iopub.status.busy":"2023-03-09T09:17:41.604476Z","iopub.status.idle":"2023-03-09T09:17:42.230776Z","shell.execute_reply":"2023-03-09T09:17:42.229155Z"},"papermill":{"duration":0.683231,"end_time":"2023-03-09T09:17:42.234775","exception":false,"start_time":"2023-03-09T09:17:41.551544","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Random Forest - Training score: 0.6923764745094994\n","Random Forest - Testing score: 0.4338522114147687\n","Gradient Boosting - Training score: 0.8408366448401194\n","Gradient Boosting - Testing score: 0.525739990198503\n","XGBoost - Training score: 0.7892983253269024\n","XGBoost - Testing score: 0.458035739693027\n"]}],"source":["from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n","import xgboost as xgb\n","\n","# Train a Random Forest Regressor\n","rf = RandomForestRegressor(n_estimators=100, random_state=42, min_samples_split=10, max_depth=10)\n","rf.fit(X_train, y_train)\n","\n","# Train a Gradient Boosting Regressor\n","gb = GradientBoostingRegressor(n_estimators=100, random_state=42, learning_rate=0.1, max_depth=3)\n","gb.fit(X_train, y_train)\n","\n","# Train an XGBoost Regressor\n","xg = xgb.XGBRegressor(n_estimators=100, random_state=42, learning_rate=0.1, max_depth=3)\n","xg.fit(X_train, y_train)\n","\n","# Evaluate the models\n","rf_train_score = rf.score(X_train, y_train)\n","rf_test_score = rf.score(X_test, y_test)\n","gb_train_score = gb.score(X_train, y_train)\n","gb_test_score = gb.score(X_test, y_test)\n","xg_train_score = xg.score(X_train, y_train)\n","xg_test_score = xg.score(X_test, y_test)\n","\n","print('Random Forest - Training score:', rf_train_score)\n","print('Random Forest - Testing score:', rf_test_score)\n","print('Gradient Boosting - Training score:', gb_train_score)\n","print('Gradient Boosting - Testing score:', gb_test_score)\n","print('XGBoost - Training score:', xg_train_score)\n","print('XGBoost - Testing score:', xg_test_score)"]},{"cell_type":"markdown","id":"4113bd71","metadata":{"papermill":{"duration":0.048891,"end_time":"2023-03-09T09:17:42.332907","exception":false,"start_time":"2023-03-09T09:17:42.284016","status":"completed"},"tags":[]},"source":["**It looks like the gradient boosting model had the best performance among the ensemble methods with a testing score of 0.5257. The random forest and XGBoost models had lower testing scores, but it's still worth considering their performance as they may perform better on different datasets.**\n","\n","**Lastly, i tried \"Hyperparameter tuning\" hyperparameter tuning is a good way to try and improve the performance of your model. One common way to perform hyperparameter tuning is by using a grid search, which tests a range of hyperparameters and finds the combination that performs the best.**"]},{"cell_type":"code","execution_count":18,"id":"3dc57d1a","metadata":{"execution":{"iopub.execute_input":"2023-03-09T09:17:42.431865Z","iopub.status.busy":"2023-03-09T09:17:42.431457Z","iopub.status.idle":"2023-03-09T09:17:58.082381Z","shell.execute_reply":"2023-03-09T09:17:58.080724Z"},"papermill":{"duration":15.702883,"end_time":"2023-03-09T09:17:58.08513","exception":false,"start_time":"2023-03-09T09:17:42.382247","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Best parameters: {'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 100}\n","Best score: 0.29386185781180474\n","Test score with best parameters: 0.42791612433516435\n"]}],"source":["from sklearn.ensemble import RandomForestRegressor\n","from sklearn.model_selection import GridSearchCV\n","\n","# Define the model\n","rf_model = RandomForestRegressor(random_state=42)\n","\n","# Define the grid of hyperparameters to search\n","param_grid = {\n","    'n_estimators': [50, 100, 200],\n","    'max_depth': [5, 10, 20],\n","    'max_features': ['sqrt', 'log2']\n","}\n","\n","# Define the grid search object\n","grid_search = GridSearchCV(rf_model, param_grid, cv=5)\n","\n","# Fit the grid search object to the data\n","grid_search.fit(X_train, y_train)\n","\n","# Print the best parameters and best score\n","print(\"Best parameters:\", grid_search.best_params_)\n","print(\"Best score:\", grid_search.best_score_)\n","\n","# Evaluate the model with best parameters on the test data\n","best_rf_model = grid_search.best_estimator_\n","test_score = best_rf_model.score(X_test, y_test)\n","print(\"Test score with best parameters:\", test_score)\n"]},{"cell_type":"markdown","id":"8bf7ab37","metadata":{"papermill":{"duration":0.05275,"end_time":"2023-03-09T09:17:58.188851","exception":false,"start_time":"2023-03-09T09:17:58.136101","status":"completed"},"tags":[]},"source":["**The best score achieved by the model with these hyperparameters on the training data is 0.2938, which is higher than the training score reported earlier without hyperparameter tuning. However, the testing score with the best parameters is 0.4279, which is lower than the testing score reported earlier without hyperparameter tuning.**\n","\n","**This suggests that the hyperparameter tuning has improved the performance of the model on the training data, but it did not generalize well to the test data.**"]},{"cell_type":"markdown","id":"cf1914f3","metadata":{"papermill":{"duration":0.051565,"end_time":"2023-03-09T09:17:58.294663","exception":false,"start_time":"2023-03-09T09:17:58.243098","status":"completed"},"tags":[]},"source":["# The problem And Final Thoughts\n","\n","***From here on the model will get complexing but we might not get better results and here why***\n","\n","***As far as i know he size of the dataset could be a limiting factor in achieving good results and can increase the risk of overfitting. With a small dataset, it may be difficult to capture the complex patterns and relationships between the features and the target variable accurately.***\n","\n","***Overfitting occurs when a model learns the noise or random fluctuations in the training data instead of the underlying patterns. This can happen when the model is too complex and has too many parameters relative to the size of the training data.***"]},{"cell_type":"markdown","id":"b5838592","metadata":{"papermill":{"duration":0.131948,"end_time":"2023-03-09T09:17:58.479996","exception":false,"start_time":"2023-03-09T09:17:58.348048","status":"completed"},"tags":[]},"source":["**Hope this notebook gave a undertanding of overfitting data even though the models aren't accurate**\n","\n","# The End"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":37.472401,"end_time":"2023-03-09T09:17:59.552828","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-03-09T09:17:22.080427","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}