1_1_13_Basketball_Player_Performance_Prediction.ipynb

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        "# **<font color='purple'>1.1.13. Basketball Player Performance Prediction</font>**\n",
        "\n",
        "## **<font color='blue'>1. Introduction to Machine Learning Concepts</font>**\n",
        "\n",
        "Machine Learning (ML) is a transformative field of artificial intelligence (AI) that empowers systems to learn from data and make predictions or decisions without explicit programming. This project focuses on using ML techniques to predict basketball players' performance based on their physical attributes and career statistics.\n",
        "\n",
        "## **<font color='blue'>2. Project Objectives</font>**\n",
        "\n",
        "### **<font color='green'>2.1 Objective: Predict the performance of basketball players.</font>**\n",
        "\n",
        "The primary objective of this project is to develop a predictive model using linear regression to forecast the performance metrics of basketball players. By leveraging statistical analysis and machine learning algorithms, students will gain insights into player evaluation and strategic decision-making in sports analytics.\n",
        "\n",
        "### **<font color='green'>2.2 Algorithm: Linear Regression</font>**\n",
        "\n",
        "Linear Regression serves as the foundational algorithm for predicting basketball player performance. This supervised learning technique establishes a linear relationship between input features (e.g., height, weight, assists, rebounds) and the target variable (points scored), enabling quantitative predictions based on historical data.\n",
        "\n",
        "### **<font color='green'>2.3 Dataset: [Kaggle - Basketball Dataset](https://www.kaggle.com/drgilermo/nba-players-stats)</font>**\n",
        "\n",
        "The project utilizes a comprehensive dataset sourced from Kaggle, encompassing detailed statistics of NBA players across seasons. This dataset provides a rich repository of player attributes, game performance metrics, and career trajectories, facilitating thorough analysis and model development.\n",
        "\n",
        "## **<font color='blue'>3. Learning Pathway and Methodology</font>**\n",
        "\n",
        "### **<font color='green'>3.1 Data Exploration and Preprocessing</font>**\n",
        "\n",
        "The journey commences with data exploration to comprehend the dataset's structure, dimensions, and inherent patterns. Preliminary data cleaning techniques are employed to address missing values, outliers, and inconsistencies, ensuring data integrity and reliability for subsequent analysis.\n",
        "\n",
        "### **<font color='green'>3.2 Exploratory Data Analysis (EDA)</font>**\n",
        "\n",
        "EDA involves visualizing data distributions, correlations, and trends using statistical plots and graphs. Students will explore the relationship between player attributes (e.g., height, weight) and performance metrics (e.g., points, assists), uncovering insights that inform feature selection and model design.\n",
        "\n",
        "### **<font color='green'>3.3 Model Development: Linear Regression</font>**\n",
        "\n",
        "The core of the project revolves around developing a robust linear regression model to predict basketball player performance. By partitioning the dataset into training and testing sets, students will learn model fitting, evaluation, and refinement techniques to optimize predictive accuracy and generalize findings.\n",
        "\n",
        "### **<font color='green'>3.4 Model Evaluation and Performance Metrics</font>**\n",
        "\n",
        "Evaluation metrics such as Mean Squared Error (MSE) and R-squared (R2) will be employed to assess the model's predictive performance. Comparative analysis of predicted versus actual outcomes will provide a quantitative measure of model efficacy, enabling students to interpret results and refine their predictive models iteratively.\n",
        "\n",
        "## **<font color='blue'>4. Practical Applications and Real-World Implications</font>**\n",
        "\n",
        "### **<font color='green'>4.1 Sports Analytics and Decision Support</font>**\n",
        "\n",
        "The project underscores the application of ML in sports analytics, offering insights into player scouting, team management, and strategic planning. By simulating real-world scenarios, students will gain practical experience in leveraging data-driven insights to optimize player performance and team dynamics.\n",
        "\n",
        "### **<font color='green'>4.2 Ethical Considerations and Bias Mitigation</font>**\n",
        "\n",
        "Discussions on ethical implications encompass fairness, transparency, and accountability in algorithmic decision-making. Emphasis will be placed on mitigating bias, ensuring equitable outcomes, and fostering responsible AI practices within the context of sports analytics and beyond.\n",
        "\n",
        "## **<font color='blue'>5. Learning Outcomes and Future Directions</font>**\n",
        "\n",
        "### **<font color='green'>5.1 Key Takeaways from the Project</font>**\n",
        "\n",
        "- Proficiency in data preprocessing, exploratory analysis, and predictive modeling using linear regression.\n",
        "- Application of ML techniques to real-world datasets for sports analytics and decision support.\n",
        "- Interpretation of evaluation metrics to assess model performance and enhance predictive accuracy.\n",
        "\n",
        "### **<font color='green'>5.2 Future Avenues for Exploration in ML</font>**\n",
        "\n",
        "The project serves as a catalyst for exploring advanced ML methodologies, including ensemble learning, deep neural networks, and reinforcement learning. Opportunities abound for investigating emerging trends in AI ethics, interpretability, and autonomous decision-making systems, preparing students for diverse career pathways in data science and AI-driven innovation.\n",
        "\n",
        "---\n",
        "\n",
        "<font color='black'>\n",
        "\n",
        "**Note:** This project encapsulates an immersive learning experience in ML, integrating theoretical concepts with practical application in sports analytics. By fostering a multidisciplinary approach, students are equipped to navigate the evolving landscape of AI and harness its transformative potential in sports, entertainment, healthcare, and beyond.\n",
        "\n",
        "</font>"
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      "source": [
        "### Importing Libraries for Data Analysis and Machine Learning\n",
        "\n",
        "#### <font color='blue'>Pandas</font> (<font color='blue'>import pandas as pd</font>)\n",
        "- Pandas is a powerful library for data manipulation and analysis in Python.\n",
        "- It provides data structures like DataFrame and Series, which are essential for handling structured data.\n",
        "- Functions such as reading data from various file formats (<font color='blue'>read_csv</font>, <font color='blue'>read_excel</font>), data manipulation (<font color='blue'>merge</font>, <font color='blue'>dropna</font>), and descriptive statistics (<font color='blue'>describe</font>) are commonly used.\n",
        "- Used extensively in this project for loading datasets, preprocessing data, and summarizing descriptive statistics.\n",
        "\n",
        "#### <font color='green'>NumPy</font> (<font color='green'>import numpy as np</font>)\n",
        "- NumPy is fundamental for numerical operations in Python.\n",
        "- It offers support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.\n",
        "- Efficiently handles computations such as mean, standard deviation, and matrix operations.\n",
        "- Integral for handling numerical data in machine learning tasks like matrix operations in linear algebra and generating arrays for model inputs.\n",
        "\n",
        "#### <font color='purple'>Matplotlib</font> (<font color='purple'>import matplotlib.pyplot as plt</font>)\n",
        "- Matplotlib is a widely-used plotting library for creating static, animated, and interactive visualizations in Python.\n",
        "- Provides a MATLAB-like interface for plotting, making it easy to generate line plots, histograms, scatter plots, etc.\n",
        "- Used in this project to visualize distributions, trends, and relationships in the data through various plots such as histograms (<font color='purple'>histplot</font>), scatter plots (<font color='purple'>scatter</font>), and line plots (<font color='purple'>plot</font>).\n",
        "\n",
        "#### <font color='orange'>Seaborn</font> (<font color='orange'>import seaborn as sns</font>)\n",
        "- Seaborn is built on top of Matplotlib and provides a higher-level interface for drawing attractive and informative statistical graphics.\n",
        "- Simplifies the creation of complex visualizations such as heatmaps (<font color='orange'>heatmap</font>), pair plots (<font color='orange'>pairplot</font>), and categorical plots.\n",
        "- Enhances the aesthetic appeal of plots with default themes and color palettes.\n",
        "- Utilized in this project to visualize relationships and distributions, and to create informative plots for exploratory data analysis (EDA).\n",
        "\n",
        "#### <font color='red'>scikit-learn</font> (<font color='red'>from sklearn...</font>)\n",
        "- scikit-learn is a comprehensive library for machine learning in Python.\n",
        "- Provides simple and efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib.\n",
        "- Includes modules for various machine learning tasks such as model selection (<font color='red'>train_test_split</font>), linear regression (<font color='red'>LinearRegression</font>), and model evaluation (<font color='red'>mean_squared_error</font>, <font color='red'>r2_score</font>).\n",
        "- Crucial for implementing machine learning models, evaluating their performance, and tuning parameters."
      ],
      "metadata": {
        "id": "g_5oJphNCdru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Import necessary libraries"
      ],
      "metadata": {
        "id": "X7vorkeB1abP"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas library for data handling"
      ],
      "metadata": {
        "id": "bsV-WzMJ16L3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy library for numerical computations"
      ],
      "metadata": {
        "id": "n6j8kKfj16Oq"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "sn4C-C7-16RW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for enhanced plotting"
      ],
      "metadata": {
        "id": "xaryZLk-16Tx"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Pv7VDgnr16Yz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Import train_test_split function"
      ],
      "metadata": {
        "id": "6VwNjnIm16b6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  # Import LinearRegression model"
      ],
      "metadata": {
        "id": "flETHb9N1aeW"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score  # Import metrics for model evaluation"
      ],
      "metadata": {
        "id": "zz4RYSmQ1aib"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SLsJu3dS1G9X"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "frHo_hnl0yLm"
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      "execution_count": null,
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      "cell_type": "markdown",
      "source": [
        "<font color='purple'>\n",
        "\n",
        "### Load the datasets and print dataset information, summary, description, and head\n",
        "\n",
        "</font>\n",
        "\n",
        "#### <font color='purple'>Notes:</font>\n",
        "- <font color='purple'>**Objective**: This step involves loading the datasets required for the analysis and exploring basic information about the datasets.</font>\n",
        "- <font color='purple'>**Datasets**:\n",
        "  - **Players Dataset**: Contains information about basketball players such as player name, height, weight, college attended, and birth year.\n",
        "  - **Seasons Stats Dataset**: Provides statistical data for basketball players across various seasons, including points scored (PTS), assists (AST), and rebounds (TRB).</font>\n",
        "- <font color='purple'>**Libraries Used**:\n",
        "  - `pandas`: Used for data manipulation and analysis.\n",
        "  - `numpy`: Essential for numerical computations.\n",
        "  - `matplotlib.pyplot`: Enables plotting and visualization.\n",
        "  - `seaborn`: Enhances the quality of visualizations.</font>\n",
        "- <font color='purple'>**Actions**:\n",
        "  - Load the `Players.csv` dataset containing player information.\n",
        "  - Load the `Seasons_Stats.csv` dataset containing season-wise statistics.\n",
        "  - Print basic information about the datasets, including number of rows and columns.\n",
        "  - Print descriptive statistics to understand the distribution of numerical variables.\n",
        "  - Display the first few rows of each dataset to get an overview of the data structure.</font>\n",
        "\n",
        "#### <font color='purple'>Key Points:</font>\n",
        "- <font color='purple'>**Data Loading**: Ensure datasets are loaded correctly to proceed with further analysis.</font>\n",
        "- <font color='purple'>**Data Exploration**: Basic exploration includes checking dimensions, data types, missing values, and initial data samples.</font>\n",
        "- <font color='purple'>**Initial Insights**: This step helps in identifying potential issues like missing data or outliers, which may require preprocessing.</font>\n",
        "- <font color='purple'>**Foundation**: Establishes a foundation for subsequent steps in data preprocessing and exploratory data analysis (EDA).</font>"
      ],
      "metadata": {
        "id": "hXVcodgzC6d2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Load the datasets and print dataset information, summary, description, and head"
      ],
      "metadata": {
        "id": "5aBRrelt0yOe"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "players_df = pd.read_csv('/content/Players.csv')  # Load Players.csv dataset"
      ],
      "metadata": {
        "id": "styzcx9N2Z13"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"=== Players Dataset Information ===\")\n",
        "print(players_df.info())  # Print dataset information"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnKiMsng8hYz",
        "outputId": "27aaf6c2-7daf-4289-d800-a7483c11b57c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Players Dataset Information ===\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3922 entries, 0 to 3921\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Unnamed: 0   3922 non-null   int64  \n",
            " 1   Player       3921 non-null   object \n",
            " 2   height       3921 non-null   float64\n",
            " 3   weight       3921 non-null   float64\n",
            " 4   collage      3573 non-null   object \n",
            " 5   born         3921 non-null   float64\n",
            " 6   birth_city   3452 non-null   object \n",
            " 7   birth_state  3439 non-null   object \n",
            "dtypes: float64(3), int64(1), object(4)\n",
            "memory usage: 245.2+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FWkmTM3S8tWb"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B0tgqoQX8tZQ"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Players Dataset Summary ===\")\n",
        "print(players_df.describe())  # Print dataset summary statistics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqEA-8JC8xE8",
        "outputId": "b133bb14-013f-421f-935b-0b7278f9292d"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Players Dataset Summary ===\n",
            "        Unnamed: 0       height       weight        born\n",
            "count  3922.000000  3921.000000  3921.000000  3921.00000\n",
            "mean   1960.500000   198.704922    94.783219  1962.37975\n",
            "std    1132.328206     9.269761    12.039515    20.33491\n",
            "min       0.000000   160.000000    60.000000  1913.00000\n",
            "25%     980.250000   190.000000    86.000000  1948.00000\n",
            "50%    1960.500000   198.000000    95.000000  1964.00000\n",
            "75%    2940.750000   206.000000   102.000000  1979.00000\n",
            "max    3921.000000   231.000000   163.000000  1997.00000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g73ifkxk8ygr"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LNJ7nMzf8yj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Players Dataset Description ===\")\n",
        "print(players_df.describe(include='all'))  # Print dataset description including categorical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZMcBQbZ8xHl",
        "outputId": "eb85ea22-0dda-471c-eebe-ec9b97b79723"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Players Dataset Description ===\n",
            "         Unnamed: 0           Player       height       weight  \\\n",
            "count   3922.000000             3921  3921.000000  3921.000000   \n",
            "unique          NaN             3921          NaN          NaN   \n",
            "top             NaN  Curly Armstrong          NaN          NaN   \n",
            "freq            NaN                1          NaN          NaN   \n",
            "mean    1960.500000              NaN   198.704922    94.783219   \n",
            "std     1132.328206              NaN     9.269761    12.039515   \n",
            "min        0.000000              NaN   160.000000    60.000000   \n",
            "25%      980.250000              NaN   190.000000    86.000000   \n",
            "50%     1960.500000              NaN   198.000000    95.000000   \n",
            "75%     2940.750000              NaN   206.000000   102.000000   \n",
            "max     3921.000000              NaN   231.000000   163.000000   \n",
            "\n",
            "                       collage        born birth_city birth_state  \n",
            "count                     3573  3921.00000       3452        3439  \n",
            "unique                     422         NaN       1264         128  \n",
            "top     University of Kentucky         NaN    Chicago  California  \n",
            "freq                        89         NaN        114         344  \n",
            "mean                       NaN  1962.37975        NaN         NaN  \n",
            "std                        NaN    20.33491        NaN         NaN  \n",
            "min                        NaN  1913.00000        NaN         NaN  \n",
            "25%                        NaN  1948.00000        NaN         NaN  \n",
            "50%                        NaN  1964.00000        NaN         NaN  \n",
            "75%                        NaN  1979.00000        NaN         NaN  \n",
            "max                        NaN  1997.00000        NaN         NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_1rRvFKe8kN7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9wsP5Z7a8kRN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Players Dataset Head ===\")\n",
        "print(players_df.head())  # Print first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6DAp6tax8hcZ",
        "outputId": "fddf7d74-d05b-4ae1-fa89-c087678aa88a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Players Dataset Head ===\n",
            "   Unnamed: 0           Player  height  weight  \\\n",
            "0           0  Curly Armstrong   180.0    77.0   \n",
            "1           1     Cliff Barker   188.0    83.0   \n",
            "2           2    Leo Barnhorst   193.0    86.0   \n",
            "3           3       Ed Bartels   196.0    88.0   \n",
            "4           4      Ralph Beard   178.0    79.0   \n",
            "\n",
            "                           collage    born   birth_city birth_state  \n",
            "0               Indiana University  1918.0          NaN         NaN  \n",
            "1           University of Kentucky  1921.0     Yorktown     Indiana  \n",
            "2         University of Notre Dame  1924.0          NaN         NaN  \n",
            "3  North Carolina State University  1925.0          NaN         NaN  \n",
            "4           University of Kentucky  1927.0  Hardinsburg    Kentucky  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hXx_IjUV8hfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "b2OovJA48hhW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seasons_df = pd.read_csv('/content/Seasons_Stats.csv')  # Load Seasons_Stats.csv dataset"
      ],
      "metadata": {
        "id": "M-MFIgzR2Z5h"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Seasons Stats Dataset Information ===\")\n",
        "print(seasons_df.info())  # Print dataset information"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbh93kqK0ya2",
        "outputId": "6e84c984-8a18-4b0b-b13e-9d13f8d21344"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Seasons Stats Dataset Information ===\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24691 entries, 0 to 24690\n",
            "Data columns (total 53 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  24691 non-null  int64  \n",
            " 1   Year        24624 non-null  float64\n",
            " 2   Player      24624 non-null  object \n",
            " 3   Pos         24624 non-null  object \n",
            " 4   Age         24616 non-null  float64\n",
            " 5   Tm          24624 non-null  object \n",
            " 6   G           24624 non-null  float64\n",
            " 7   GS          18233 non-null  float64\n",
            " 8   MP          24138 non-null  float64\n",
            " 9   PER         24101 non-null  float64\n",
            " 10  TS%         24538 non-null  float64\n",
            " 11  3PAr        18839 non-null  float64\n",
            " 12  FTr         24525 non-null  float64\n",
            " 13  ORB%        20792 non-null  float64\n",
            " 14  DRB%        20792 non-null  float64\n",
            " 15  TRB%        21571 non-null  float64\n",
            " 16  AST%        22555 non-null  float64\n",
            " 17  STL%        20792 non-null  float64\n",
            " 18  BLK%        20792 non-null  float64\n",
            " 19  TOV%        19582 non-null  float64\n",
            " 20  USG%        19640 non-null  float64\n",
            " 21  blanl       0 non-null      float64\n",
            " 22  OWS         24585 non-null  float64\n",
            " 23  DWS         24585 non-null  float64\n",
            " 24  WS          24585 non-null  float64\n",
            " 25  WS/48       24101 non-null  float64\n",
            " 26  blank2      0 non-null      float64\n",
            " 27  OBPM        20797 non-null  float64\n",
            " 28  DBPM        20797 non-null  float64\n",
            " 29  BPM         20797 non-null  float64\n",
            " 30  VORP        20797 non-null  float64\n",
            " 31  FG          24624 non-null  float64\n",
            " 32  FGA         24624 non-null  float64\n",
            " 33  FG%         24525 non-null  float64\n",
            " 34  3P          18927 non-null  float64\n",
            " 35  3PA         18927 non-null  float64\n",
            " 36  3P%         15416 non-null  float64\n",
            " 37  2P          24624 non-null  float64\n",
            " 38  2PA         24624 non-null  float64\n",
            " 39  2P%         24496 non-null  float64\n",
            " 40  eFG%        24525 non-null  float64\n",
            " 41  FT          24624 non-null  float64\n",
            " 42  FTA         24624 non-null  float64\n",
            " 43  FT%         23766 non-null  float64\n",
            " 44  ORB         20797 non-null  float64\n",
            " 45  DRB         20797 non-null  float64\n",
            " 46  TRB         24312 non-null  float64\n",
            " 47  AST         24624 non-null  float64\n",
            " 48  STL         20797 non-null  float64\n",
            " 49  BLK         20797 non-null  float64\n",
            " 50  TOV         19645 non-null  float64\n",
            " 51  PF          24624 non-null  float64\n",
            " 52  PTS         24624 non-null  float64\n",
            "dtypes: float64(49), int64(1), object(3)\n",
            "memory usage: 10.0+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mw1GhbPY89_8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O8UelxyX8-Cn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Seasons Stats Dataset Summary ===\")\n",
        "print(seasons_df.describe())  # Print dataset summary statistics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HSLpD8Z8-Ev",
        "outputId": "d80b9efc-a5ab-4ece-c0ec-a78be708bfe4"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Seasons Stats Dataset Summary ===\n",
            "         Unnamed: 0          Year           Age             G            GS  \\\n",
            "count  24691.000000  24624.000000  24616.000000  24624.000000  18233.000000   \n",
            "mean   12345.000000   1992.594989     26.664405     50.837110     23.593375   \n",
            "std     7127.822084     17.429594      3.841892     26.496161     28.632387   \n",
            "min        0.000000   1950.000000     18.000000      1.000000      0.000000   \n",
            "25%     6172.500000   1981.000000     24.000000     27.000000      0.000000   \n",
            "50%    12345.000000   1996.000000     26.000000     58.000000      8.000000   \n",
            "75%    18517.500000   2007.000000     29.000000     75.000000     45.000000   \n",
            "max    24690.000000   2017.000000     44.000000     88.000000     83.000000   \n",
            "\n",
            "                 MP           PER           TS%          3PAr           FTr  \\\n",
            "count  24138.000000  24101.000000  24538.000000  18839.000000  24525.000000   \n",
            "mean    1209.720317     12.479071      0.493001      0.158604      0.325455   \n",
            "std      941.146575      6.039014      0.094469      0.187495      0.218971   \n",
            "min        0.000000    -90.600000      0.000000      0.000000      0.000000   \n",
            "25%      340.000000      9.800000      0.458000      0.005000      0.208000   \n",
            "50%     1053.000000     12.700000      0.506000      0.064000      0.296000   \n",
            "75%     1971.000000     15.600000      0.544000      0.288000      0.400000   \n",
            "max     3882.000000    129.100000      1.136000      1.000000      6.000000   \n",
            "\n",
            "       ...           FT%           ORB           DRB           TRB  \\\n",
            "count  ...  23766.000000  20797.000000  20797.000000  24312.000000   \n",
            "mean   ...      0.719279     62.189210    147.199404    224.637381   \n",
            "std    ...      0.141824     67.324881    145.921912    228.190203   \n",
            "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
            "25%    ...      0.657000     12.000000     33.000000     51.000000   \n",
            "50%    ...      0.743000     38.000000    106.000000    159.000000   \n",
            "75%    ...      0.808000     91.000000    212.000000    322.000000   \n",
            "max    ...      1.000000    587.000000   1111.000000   2149.000000   \n",
            "\n",
            "                AST           STL           BLK           TOV            PF  \\\n",
            "count  24624.000000  20797.000000  20797.000000  19645.000000  24624.000000   \n",
            "mean     114.852623     39.897052     24.470260     73.939832    116.339222   \n",
            "std      135.863913     38.713053     36.935084     67.713803     84.791873   \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%       19.000000      9.000000      3.000000     18.000000     39.000000   \n",
            "50%       68.000000     29.000000     11.000000     55.000000    109.000000   \n",
            "75%      160.000000     60.000000     29.000000    112.000000    182.000000   \n",
            "max     1164.000000    301.000000    456.000000    464.000000    386.000000   \n",
            "\n",
            "                PTS  \n",
            "count  24624.000000  \n",
            "mean     510.116350  \n",
            "std      492.922981  \n",
            "min        0.000000  \n",
            "25%      106.000000  \n",
            "50%      364.000000  \n",
            "75%      778.000000  \n",
            "max     4029.000000  \n",
            "\n",
            "[8 rows x 50 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2ydTH4FM9GxL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UGV-ZCXO9G0T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Seasons Stats Dataset Description ===\")\n",
        "print(seasons_df.describe(include='all'))  # Print dataset description including categorical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ir9iMviN9G3p",
        "outputId": "894e8692-6e0d-4ea6-816b-5cad3088f7bd"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Seasons Stats Dataset Description ===\n",
            "          Unnamed: 0          Year         Player    Pos           Age     Tm  \\\n",
            "count   24691.000000  24624.000000          24624  24624  24616.000000  24624   \n",
            "unique           NaN           NaN           3921     23           NaN     69   \n",
            "top              NaN           NaN  Eddie Johnson     PF           NaN    TOT   \n",
            "freq             NaN           NaN             33   4966           NaN   2123   \n",
            "mean    12345.000000   1992.594989            NaN    NaN     26.664405    NaN   \n",
            "std      7127.822084     17.429594            NaN    NaN      3.841892    NaN   \n",
            "min         0.000000   1950.000000            NaN    NaN     18.000000    NaN   \n",
            "25%      6172.500000   1981.000000            NaN    NaN     24.000000    NaN   \n",
            "50%     12345.000000   1996.000000            NaN    NaN     26.000000    NaN   \n",
            "75%     18517.500000   2007.000000            NaN    NaN     29.000000    NaN   \n",
            "max     24690.000000   2017.000000            NaN    NaN     44.000000    NaN   \n",
            "\n",
            "                   G            GS            MP           PER  ...  \\\n",
            "count   24624.000000  18233.000000  24138.000000  24101.000000  ...   \n",
            "unique           NaN           NaN           NaN           NaN  ...   \n",
            "top              NaN           NaN           NaN           NaN  ...   \n",
            "freq             NaN           NaN           NaN           NaN  ...   \n",
            "mean       50.837110     23.593375   1209.720317     12.479071  ...   \n",
            "std        26.496161     28.632387    941.146575      6.039014  ...   \n",
            "min         1.000000      0.000000      0.000000    -90.600000  ...   \n",
            "25%        27.000000      0.000000    340.000000      9.800000  ...   \n",
            "50%        58.000000      8.000000   1053.000000     12.700000  ...   \n",
            "75%        75.000000     45.000000   1971.000000     15.600000  ...   \n",
            "max        88.000000     83.000000   3882.000000    129.100000  ...   \n",
            "\n",
            "                 FT%           ORB           DRB           TRB           AST  \\\n",
            "count   23766.000000  20797.000000  20797.000000  24312.000000  24624.000000   \n",
            "unique           NaN           NaN           NaN           NaN           NaN   \n",
            "top              NaN           NaN           NaN           NaN           NaN   \n",
            "freq             NaN           NaN           NaN           NaN           NaN   \n",
            "mean        0.719279     62.189210    147.199404    224.637381    114.852623   \n",
            "std         0.141824     67.324881    145.921912    228.190203    135.863913   \n",
            "min         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
            "25%         0.657000     12.000000     33.000000     51.000000     19.000000   \n",
            "50%         0.743000     38.000000    106.000000    159.000000     68.000000   \n",
            "75%         0.808000     91.000000    212.000000    322.000000    160.000000   \n",
            "max         1.000000    587.000000   1111.000000   2149.000000   1164.000000   \n",
            "\n",
            "                 STL           BLK           TOV            PF           PTS  \n",
            "count   20797.000000  20797.000000  19645.000000  24624.000000  24624.000000  \n",
            "unique           NaN           NaN           NaN           NaN           NaN  \n",
            "top              NaN           NaN           NaN           NaN           NaN  \n",
            "freq             NaN           NaN           NaN           NaN           NaN  \n",
            "mean       39.897052     24.470260     73.939832    116.339222    510.116350  \n",
            "std        38.713053     36.935084     67.713803     84.791873    492.922981  \n",
            "min         0.000000      0.000000      0.000000      0.000000      0.000000  \n",
            "25%         9.000000      3.000000     18.000000     39.000000    106.000000  \n",
            "50%        29.000000     11.000000     55.000000    109.000000    364.000000  \n",
            "75%        60.000000     29.000000    112.000000    182.000000    778.000000  \n",
            "max       301.000000    456.000000    464.000000    386.000000   4029.000000  \n",
            "\n",
            "[11 rows x 53 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bBeczGQA0yeA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t9gsJSyT9K-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n=== Seasons Stats Dataset Head ===\")\n",
        "print(seasons_df.head())  # Print first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8lpJ8j89LB4",
        "outputId": "1ff6c9e0-059b-4e9b-e5a7-a3d2c33d2428"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Seasons Stats Dataset Head ===\n",
            "   Unnamed: 0    Year           Player  Pos   Age   Tm     G  GS  MP  PER  \\\n",
            "0           0  1950.0  Curly Armstrong  G-F  31.0  FTW  63.0 NaN NaN  NaN   \n",
            "1           1  1950.0     Cliff Barker   SG  29.0  INO  49.0 NaN NaN  NaN   \n",
            "2           2  1950.0    Leo Barnhorst   SF  25.0  CHS  67.0 NaN NaN  NaN   \n",
            "3           3  1950.0       Ed Bartels    F  24.0  TOT  15.0 NaN NaN  NaN   \n",
            "4           4  1950.0       Ed Bartels    F  24.0  DNN  13.0 NaN NaN  NaN   \n",
            "\n",
            "   ...    FT%  ORB  DRB  TRB    AST  STL  BLK  TOV     PF    PTS  \n",
            "0  ...  0.705  NaN  NaN  NaN  176.0  NaN  NaN  NaN  217.0  458.0  \n",
            "1  ...  0.708  NaN  NaN  NaN  109.0  NaN  NaN  NaN   99.0  279.0  \n",
            "2  ...  0.698  NaN  NaN  NaN  140.0  NaN  NaN  NaN  192.0  438.0  \n",
            "3  ...  0.559  NaN  NaN  NaN   20.0  NaN  NaN  NaN   29.0   63.0  \n",
            "4  ...  0.548  NaN  NaN  NaN   20.0  NaN  NaN  NaN   27.0   59.0  \n",
            "\n",
            "[5 rows x 53 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lPbSXIXC9OeO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IOTMgZdY9Ohr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Data preprocessing and exploration</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Prepare the data for analysis by handling missing values and selecting relevant features from the merged dataset.\n",
        "- <font color='green'><b>Preprocessing:</b></font>\n",
        "  - <font color='darkorange'><b>Merge:</b></font> Combine relevant columns from <font color='brown'><b>players_df</b></font> and <font color='brown'><b>seasons_df</b></font> based on the 'Player' column.\n",
        "  - <font color='darkorange'><b>Handling Missing Values:</b></font> Drop rows with missing values to ensure data integrity.\n",
        "- <font color='purple'><b>Libraries Used:</b></font>\n",
        "  - <font color='brown'><b>pandas:</b></font> Essential for data manipulation and handling missing values.\n",
        "  - <font color='brown'><b>numpy:</b></font> Used for numerical computations and array operations.\n",
        "- <font color='purple'><b>Exploration:</b></font>\n",
        "  - Explore the distribution of key variables like height, weight, assists (AST), and rebounds (TRB) through histograms.\n",
        "  - Visualize relationships between numerical features using pair plots and correlation heatmaps.\n",
        "- <font color='green'><b>Actions:</b></font>\n",
        "  - Merge relevant columns from <font color='brown'><b>seasons_df</b></font> into <font color='brown'><b>players_df</b></font> based on 'Player' column.\n",
        "  - Drop rows containing missing values to ensure a clean dataset for analysis.\n",
        "\n",
        "<font color='purple'><b> Key Points:</b></font>\n",
        "- <font color='green'><b>Data Integrity:</b></font> Ensure merged dataset is clean and suitable for further analysis.\n",
        "- <font color='green'><b>Feature Selection:</b></font> Focus on relevant features like height, weight, AST, and TRB for initial exploration.\n",
        "- <font color='green'><b>Visualization:</b></font> Use histograms, pair plots, and correlation heatmaps to gain insights into data distributions and relationships.\n",
        "- <font color='green'><b>Foundation for Analysis:</b></font> Prepare data for subsequent steps such as model training and evaluation."
      ],
      "metadata": {
        "id": "0t-DMu6oDON3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Data preprocessing and exploration"
      ],
      "metadata": {
        "id": "w-2u-s_80yiy"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge relevant columns from seasons_df into players_df"
      ],
      "metadata": {
        "id": "g-1G6WEC0AZ-"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = players_df[['Player', 'height', 'weight', 'collage', 'born']].merge(\n",
        "    seasons_df[['Player', 'PTS', 'AST', 'TRB']], on='Player', how='inner')  # Merge on 'Player' column"
      ],
      "metadata": {
        "id": "MFI5bUMSzAIT"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Retain only relevant columns for analysis"
      ],
      "metadata": {
        "id": "Buy1MKmDzALS"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "twBnhNAuzANs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling missing values if any"
      ],
      "metadata": {
        "id": "7zu7Mo1hx5ef"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.dropna(inplace=True)  # Drop rows with missing values"
      ],
      "metadata": {
        "id": "wy_uMKcNwBuM"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"   Merged dataset shape:\", merged_df.shape)  # Print shape of merged dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nGx7xggv0KO",
        "outputId": "86a785a0-10e7-412a-d225-7d1a80838b22"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Merged dataset shape: (22103, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D9yVrMk-vvhE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gq4Xc5UdvttU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b>  Exploratory Data Analysis (EDA)</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Explore the dataset visually to understand the distribution and relationships of key variables.\n",
        "- <font color='purple'><b>Libraries Used:</b></font>\n",
        "  - <font color='brown'><b>matplotlib:</b></font> Utilized for creating visualizations such as histograms and scatter plots.\n",
        "  - <font color='brown'><b>seaborn:</b></font> Used for enhancing the style and aesthetics of plots.\n",
        "- <font color='purple'><b>Exploration:</b></font>\n",
        "  - <font color='darkorange'><b>Distribution Plots:</b></font> Visualize the distribution of height, weight, assists (AST), and rebounds (TRB) using histograms.\n",
        "  - <font color='darkorange'><b>Pairplot:</b></font> Create pair plots to visualize pairwise relationships between numerical features.\n",
        "  - <font color='darkorange'><b>Correlation Heatmap:</b></font> Generate a heatmap to visualize the correlation between numerical features.\n",
        "- <font color='purple'><b>Visualization:</b></font>\n",
        "  - Customize plots with different colors to enhance readability and insights.\n",
        "  - Save plots as images for future reference and presentation.\n",
        "- <font color='green'><b>Actions:</b></font>\n",
        "  - Utilize histograms to analyze the distribution of individual variables such as height and weight.\n",
        "  - Create pair plots to observe relationships between multiple variables simultaneously.\n",
        "  - Generate a correlation heatmap to identify correlations between features.\n",
        "  \n",
        "<font color='purple'><b> Key Points:</b></font>\n",
        "- <font color='green'><b>Visual Insight:</b></font> Gain insights into the distribution and relationships among variables through visualizations.\n",
        "- <font color='green'><b>Plot Customization:</b></font> Enhance plots with different colors and styles to improve clarity and aesthetics.\n",
        "- <font color='green'><b>Decision Making:</b></font> Use EDA findings to inform decisions for subsequent data preprocessing and model development steps."
      ],
      "metadata": {
        "id": "WGsF5dSCDpf2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "6WeP7DMbvtwU"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Distribution of height"
      ],
      "metadata": {
        "id": "Ag55mzO-vtzc"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.histplot(merged_df['height'], bins=30, kde=True, color='purple')  # Create histogram plot for 'height' in purple\n",
        "plt.title('Distribution of Height')  # Set plot title\n",
        "plt.xlabel('Height (cm)')  # Set x-axis label\n",
        "plt.ylabel('Frequency')  # Set y-axis label\n",
        "plt.savefig('height_distribution.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "EXXfGsffvt2c",
        "outputId": "200c4e93-fedd-490c-a182-a00e404cac2b"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gw1pbQ0Nvt5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vrs9JBH7vt77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of weight"
      ],
      "metadata": {
        "id": "8rWNCkqNvt_E"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.histplot(merged_df['weight'], bins=30, kde=True, color='pink')  # Create histogram plot for 'weight' in pink\n",
        "plt.title('Distribution of Weight')  # Set plot title\n",
        "plt.xlabel('Weight (kg)')  # Set x-axis label\n",
        "plt.ylabel('Frequency')  # Set y-axis label\n",
        "plt.savefig('weight_distribution.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "vMEWdwGcvuBo",
        "outputId": "290f0a80-8c5a-4790-9a01-1063b71d1992"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vDqiToL4vuEU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Akfc6FlvuGi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of assists (AST)"
      ],
      "metadata": {
        "id": "7WZxEBxcvuIn"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.histplot(merged_df['AST'], bins=30, kde=True, color='red')  # Create histogram plot for 'AST' in red\n",
        "plt.title('Distribution of Assists (AST)')  # Set plot title\n",
        "plt.xlabel('AST')  # Set x-axis label\n",
        "plt.ylabel('Frequency')  # Set y-axis label\n",
        "plt.savefig('AST_distribution.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "UA6M_lmNvuKy",
        "outputId": "369b48ea-64ae-45e5-a7be-d4b1ba59059d"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xOcXBc6jvuOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TiYNRz8J3e1X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of rebounds (TRB)"
      ],
      "metadata": {
        "id": "fBkfQ4z73e4X"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.histplot(merged_df['TRB'], bins=30, kde=True, color='purple')  # Create histogram plot for 'TRB' in purple\n",
        "plt.title('Distribution of Rebounds (TRB)')  # Set plot title\n",
        "plt.xlabel('TRB')  # Set x-axis label\n",
        "plt.ylabel('Frequency')  # Set y-axis label\n",
        "plt.savefig('TRB_distribution.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NQmONHnr3e7c",
        "outputId": "ccf9c431-3e6b-48ed-d11b-269e15ad3f33"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qfFg4_cX3p-Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bnP8LzKV3qBP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pairplot for numerical features"
      ],
      "metadata": {
        "id": "7Ij9nxca3qEk"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Set figure size\n",
        "sns.pairplot(merged_df[['height', 'weight', 'AST', 'TRB']])  # Create pairplot for selected numerical features\n",
        "plt.suptitle('Pairplot of Numerical Features', y=1.02)  # Set plot title\n",
        "plt.savefig('pairplot.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "hrMAsAk63qGx",
        "outputId": "cb64bd88-3f8c-428f-b34b-9a0740acffde"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A3GMjoMI31zE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RadYkKCO32AW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap"
      ],
      "metadata": {
        "id": "HNf-QI8g32EL"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.heatmap(merged_df[['height', 'weight', 'PTS', 'AST', 'TRB']].corr(), annot=True, cmap='coolwarm')  # Create heatmap\n",
        "plt.title('Correlation Heatmap')  # Set plot title\n",
        "plt.savefig('correlation_heatmap.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "yTyNh_Ky3qNJ",
        "outputId": "665f4573-74ad-45fb-8b87-5f1031791443"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YMPFV7z04Kcg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eSG_6LC04Kfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CVjMldix4KwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Descriptive Statistics</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Compute and analyze summary statistics of numerical columns in the dataset.\n",
        "- <font color='purple'><b>Libraries Used:</b></font>\n",
        "  - <font color='brown'><b>pandas:</b></font> Utilized for data manipulation and computation of descriptive statistics.\n",
        "- <font color='purple'><b>Summary Statistics:</b></font>\n",
        "  - Use pandas to calculate statistical measures such as mean, median, standard deviation, min, max, and quartiles.\n",
        "  - Display summary statistics to understand the central tendency, dispersion, and shape of data distributions.\n",
        "- <font color='purple'><b>Insights:</b></font>\n",
        "  - Identify outliers or anomalies in data distribution.\n",
        "  - Gain insights into the range and variability of numerical features.\n",
        "  \n",
        "<font color='purple'><b> Key Points:</b></font>\n",
        "- <font color='green'><b>Data Analysis:</b></font> Use descriptive statistics to summarize and interpret numerical data characteristics.\n",
        "- <font color='green'><b>Decision Making:</b></font> Inform data preprocessing and model selection based on statistical insights."
      ],
      "metadata": {
        "id": "wo9-dodpD0gg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Descriptive statistics"
      ],
      "metadata": {
        "id": "AIUu0ZBN4LEZ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n Descriptive Statistics:\")\n",
        "print(merged_df.describe())  # Print summary statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKzagYtd3e_X",
        "outputId": "dbd347ee-d906-44df-e293-ea54908337ce"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Descriptive Statistics:\n",
            "             height        weight          born           PTS           AST  \\\n",
            "count  22103.000000  22103.000000  22103.000000  22103.000000  22103.000000   \n",
            "mean     199.021264     94.969823   1963.011446    508.354748    115.395919   \n",
            "std        9.195171     11.665455     16.990797    494.034213    137.206935   \n",
            "min      160.000000     60.000000   1915.000000      0.000000      0.000000   \n",
            "25%      193.000000     86.000000   1952.000000    104.000000     19.000000   \n",
            "50%      201.000000     95.000000   1964.000000    362.000000     67.000000   \n",
            "75%      206.000000    103.000000   1976.000000    777.000000    162.000000   \n",
            "max      231.000000    163.000000   1997.000000   4029.000000   1164.000000   \n",
            "\n",
            "                TRB  \n",
            "count  22103.000000  \n",
            "mean     222.333620  \n",
            "std      228.420661  \n",
            "min        0.000000  \n",
            "25%       50.000000  \n",
            "50%      157.000000  \n",
            "75%      317.000000  \n",
            "max     2149.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P2k7k2l13fCP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jYY0jfRg3fER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Handling special characters in column names</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Clean column names by removing special characters that may hinder data analysis and model training.\n",
        "- <font color='purple'><b>Libraries Used:</b></font>\n",
        "  - <font color='brown'><b>pandas:</b></font> Essential for manipulating DataFrame column names efficiently.\n",
        "- <font color='purple'><b>Procedure:</b></font>\n",
        "  - Utilize pandas' `.columns.str.replace()` method to remove special characters such as `#`, `@`, `&`, etc.\n",
        "  - Ensure column names are consistent and conform to standard naming conventions.\n",
        "- <font color='purple'><b>Impact:</b></font>\n",
        "  - Facilitates seamless data handling and enhances clarity during exploratory data analysis (EDA) and model development.\n",
        "  \n",
        "<font color='purple'><b> Key Considerations:</b></font>\n",
        "- <font color='green'><b>Data Integrity:</b></font> Maintain data integrity by standardizing column names.\n",
        "- <font color='green'><b>Compatibility:</b></font> Ensure compatibility with downstream data processing and modeling steps."
      ],
      "metadata": {
        "id": "oc7m8IPbEEMS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Handling special characters in column names"
      ],
      "metadata": {
        "id": "kyVYXr2N4S84"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.columns = merged_df.columns.str.replace('[#,@,&]', '')  # Remove special characters from column names"
      ],
      "metadata": {
        "id": "TbJmMZST4TEx"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9DD1C4Ei4THi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EXyhM-Jf4hK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Model training</b></font>\n",
        "\n",
        "<font color='purple'><b>Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Train a machine learning model to predict basketball player performance based on selected features.\n",
        "- <font color='purple'><b>Algorithm:</b></font> Linear Regression\n",
        "- <font color='purple'><b>Dataset:</b></font> [Kaggle - Basketball Dataset](https://www.kaggle.com/drgilermo/nba-players-stats)\n",
        "- <font color='purple'><b>Libraries Used:</b></font>\n",
        "  - <font color='brown'><b>sklearn:</b></font> For model training and evaluation.\n",
        "- <font color='purple'><b>Procedure:</b></font>\n",
        "  - Define features (`X`) and target (`y`) variables from the preprocessed dataset.\n",
        "  - Split the data into training and testing sets using `train_test_split()`.\n",
        "  - Initialize a Linear Regression model using `LinearRegression()`.\n",
        "  - Train the model on the training data using `.fit(X_train, y_train)`.\n",
        "- <font color='purple'><b>Model Evaluation:</b></font>\n",
        "  - Predict performance (Points - `PTS`) on the test set.\n",
        "  - Evaluate model performance using Mean Squared Error (MSE) and R-squared (R2) scores.\n",
        "- <font color='purple'><b>Outcome:</b></font>\n",
        "  - Obtain insights into how well the model predicts basketball player performance based on height, weight, assists (AST), and rebounds (TRB)."
      ],
      "metadata": {
        "id": "D7YLMrJrEOJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Model training"
      ],
      "metadata": {
        "id": "zT4bz4vq4a_o"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define features (X) and target (y)"
      ],
      "metadata": {
        "id": "Axsl9A1N4bDQ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = merged_df[['height', 'weight', 'AST', 'TRB']]  # Select features for model training"
      ],
      "metadata": {
        "id": "bKFsivPp4bGY"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = merged_df['PTS']  # Select target variable"
      ],
      "metadata": {
        "id": "3Puyy1qQ4bJE"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bkFiSour4bLW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h-qD6YGH4bOI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets"
      ],
      "metadata": {
        "id": "aGdEJMZz4wp7"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split data"
      ],
      "metadata": {
        "id": "1V0tPqeE4ws2"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WSgxdhy04wyZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize Linear Regression model"
      ],
      "metadata": {
        "id": "leHew-3y4w01"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()  # Create Linear Regression model object"
      ],
      "metadata": {
        "id": "60I9cKz34w4C"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WsCfXvwA4w6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model"
      ],
      "metadata": {
        "id": "l5gSTbiB4-SD"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)  # Train model on training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "VmrKc2bl4-Vz",
        "outputId": "d033a482-bc19-4dc2-d87b-6d97f34e2903"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"   Model training completed.\")  # Print message indicating model training completion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbjDFm0W4-Yy",
        "outputId": "d6ba1d81-9cbb-4703-ff2e-f23f58b4943e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Model training completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rTmtSYV04-bU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1mzQSP934-d0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Model evaluation</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Objective:</b></font> Evaluate the performance of the Linear Regression model in predicting basketball player performance.\n",
        "- <font color='purple'><b>Metrics:</b></font>\n",
        "  - Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values.\n",
        "  - R-squared (R2): Indicates the proportion of the variance in the dependent variable (PTS) that is predictable from the independent variables (height, weight, AST, TRB).\n",
        "- <font color='purple'><b>Outcome:</b></font>\n",
        "  - Interpret the model's effectiveness in predicting PTS based on the chosen features.\n",
        "- <font color='purple'><b>Visualization:</b></font>\n",
        "  - Scatter plot comparing actual PTS vs. predicted PTS to visually assess model performance."
      ],
      "metadata": {
        "id": "n8P65K0wEeUG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8: Model evaluation"
      ],
      "metadata": {
        "id": "ZIFvwv-A4-fz"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Predict on the test set"
      ],
      "metadata": {
        "id": "9e6u4f6_5PCC"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)  # Make predictions on test data"
      ],
      "metadata": {
        "id": "f30LT47W5PGQ"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GevtoWMa5PJN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Calculate metrics"
      ],
      "metadata": {
        "id": "chG0mZ_q5PMR"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)  # Calculate Mean Squared Error"
      ],
      "metadata": {
        "id": "lb_ukhTW5PO8"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2 = r2_score(y_test, y_pred)  # Calculate R-squared"
      ],
      "metadata": {
        "id": "cCGTn2cT5lZx"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u9MG-Op35lc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'   Mean Squared Error (MSE): {mse:.2f}')  # Print MSE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRD_HxTw5lgF",
        "outputId": "693a6dc4-1013-4b72-c765-c3611d428642"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Mean Squared Error (MSE): 58415.20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'   R-squared (R2): {r2:.2f}')  # Print R-squared"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZlRw3Zb5ljO",
        "outputId": "8eb0cb80-f406-441d-e83a-597a28095c35"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   R-squared (R2): 0.76\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mc9nOnY_5ll_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XLXVv3dA53HZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Save predicted values to a CSV file</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Purpose:</b></font> Save the predicted values of basketball player points (PTS) to a CSV file for further analysis or reporting.\n",
        "- <font color='purple'><b>File Output:</b></font>\n",
        "  - CSV file named 'predictions.csv' containing columns for actual PTS and predicted PTS.\n",
        "- <font color='purple'><b>Usage:</b></font>\n",
        "  - Provides a structured format to review and analyze the model's predictions against actual values.\n",
        "- <font color='purple'><b>Post-processing:</b></font>\n",
        "  - Enables easy integration of predicted values into other data analysis tools or reports."
      ],
      "metadata": {
        "id": "A0F0qznaEu0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Save predicted values to a CSV file"
      ],
      "metadata": {
        "id": "g827luY853Kk"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df = pd.DataFrame({'Actual PTS': y_test, 'Predicted PTS': y_pred})  # Create DataFrame with actual and predicted values\n"
      ],
      "metadata": {
        "id": "ymsu0Efq53Nf"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df.to_csv('predictions.csv', index=False)  # Save predictions to CSV file"
      ],
      "metadata": {
        "id": "ERODaI9i53PG"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"   Predicted values saved to 'predictions.csv'.\")  # Print message indicating file save"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4FCqsVs53si",
        "outputId": "f1f5ec5f-3dd7-45f4-9d92-cec361f12b86"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Predicted values saved to 'predictions.csv'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ggkCgTBZ53vt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uc3IvlzW6G1j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Example prediction</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Purpose:</b></font> Demonstrate how the trained model predicts points (PTS) for a hypothetical basketball player.\n",
        "- <font color='purple'><b>Example Input:</b></font>\n",
        "  - Input features: Height, weight, assists (AST), rebounds (TRB) for a player.\n",
        "- <font color='purple'><b>Prediction Output:</b></font>\n",
        "  - Predicted points (PTS) based on the input features using the trained linear regression model.\n",
        "- <font color='purple'><b>Application:</b></font>\n",
        "  - Illustrates the practical application of the model for predicting performance metrics.\n",
        "- <font color='purple'><b>Educational Use:</b></font>\n",
        "  - Helps students understand how machine learning models make predictions in real-world scenarios."
      ],
      "metadata": {
        "id": "wlXlT8sBE3Mn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 10: Example prediction"
      ],
      "metadata": {
        "id": "JnzzdvrG6G4T"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "player_features = np.array([[200, 100, 50, 30]])  # Example input features (height, weight, AST, TRB)"
      ],
      "metadata": {
        "id": "MKcyQTDw6G7e"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_points = model.predict(player_features)  # Predict points for example input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_lx7y2J6G-S",
        "outputId": "ec9edbc3-dda8-454f-f3bb-379e93d1f493"
      },
      "execution_count": 74,
      "outputs": [
        {
          "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 LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'   Predicted Points for example input: {predicted_points[0]:.2f}')  # Print predicted points"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMDMWHTu6HAo",
        "outputId": "1ffc478c-5121-43bf-82af-e25fd9f4da7d"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Predicted Points for example input: 154.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hgE45nO16WuG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DE1T6TJg6Xl7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'><b> Plotting predicted vs actual values</b></font>\n",
        "\n",
        "<font color='purple'><b> Notes:</b></font>\n",
        "- <font color='green'><b>Purpose:</b></font> Visualize and compare the actual versus predicted points (PTS) using a scatter plot.\n",
        "- <font color='purple'><b>Plot Details:</b></font>\n",
        "  - Scatter plot: Displays actual PTS on the x-axis and predicted PTS on the y-axis.\n",
        "  - Line of perfect prediction (diagonal): Indicates where actual equals predicted values.\n",
        "  - Color coding: Blue color for data points, red dashed line for the line of perfect prediction.\n",
        "- <font color='purple'><b>Insight:</b></font>\n",
        "  - Assess how well the model predicts player performance based on the closeness of points to the diagonal line.\n",
        "- <font color='purple'><b>Educational Use:</b></font>\n",
        "  - Teaches students about model evaluation through visual representation.\n",
        "  - Demonstrates the effectiveness of the trained model in predicting outcomes."
      ],
      "metadata": {
        "id": "nIq7GXPmFBIt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 11: Plotting predicted vs actual values"
      ],
      "metadata": {
        "id": "Jzqoexgj6Xqb"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "plt.scatter(y_test, y_pred, color='blue', alpha=0.5)  # Scatter plot of actual vs predicted values\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], linestyle='--', color='red')  # Plot line of perfect prediction\n",
        "plt.xlabel('Actual PTS')  # Set x-axis label\n",
        "plt.ylabel('Predicted PTS')  # Set y-axis label\n",
        "plt.title('Actual vs Predicted PTS')  # Set plot title\n",
        "plt.savefig('actual_vs_predicted.png')  # Save the plot as an image\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Gf3EsG_X6Xuk",
        "outputId": "f7471859-878b-41cf-abf7-ee6f59519478"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SNb5_4W16XyS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bwIwv6OM6X1f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Informative Summary"
      ],
      "metadata": {
        "id": "LljvmK3L6HEi"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "informative_summary = \"\"\"\n",
        "=== Informative Summary ===\n",
        "1. Dataset loading: Players and Seasons_Stats datasets loaded.\n",
        "2. Dataset information:\n",
        "   - Players Dataset: Shape (rows, columns) {}\n",
        "   - Seasons Stats Dataset: Shape (rows, columns) {}\n",
        "3. Exploratory Data Analysis (EDA):\n",
        "   - Distribution plots: Height (purple), Weight (pink), AST (red), TRB (purple).\n",
        "   - Pairplot of numerical features.\n",
        "   - Correlation heatmap between features.\n",
        "4. Descriptive Statistics:\n",
        "   - Summary statistics for numerical columns.\n",
        "5. Model Training:\n",
        "   - Linear Regression model trained using height, weight, AST, and TRB.\n",
        "6. Model Evaluation:\n",
        "   - Mean Squared Error (MSE): {:.2f}\n",
        "   - R-squared (R2): {:.2f}\n",
        "7. Example Prediction:\n",
        "   - Predicted Points for example input: {:.2f}\n",
        "8. Saved Outputs:\n",
        "   - Predicted values saved to 'predictions.csv'.\n",
        "   - EDA plots saved as images.\n",
        "   - Actual vs Predicted PTS plot saved as 'actual_vs_predicted.png'.\n",
        "\"\"\".format(players_df.shape, seasons_df.shape, mse, r2, predicted_points[0])"
      ],
      "metadata": {
        "id": "-wPIzr876pSr"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6669w_Y96vuK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(informative_summary)  # Print informative summary to console"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h68VnRKI_pSN",
        "outputId": "37f0ab89-6f85-4c7a-8175-c90e69a2785b"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Informative Summary ===\n",
            "1. Dataset loading: Players and Seasons_Stats datasets loaded.\n",
            "2. Dataset information:\n",
            "   - Players Dataset: Shape (rows, columns) (3922, 8)\n",
            "   - Seasons Stats Dataset: Shape (rows, columns) (24691, 53)\n",
            "3. Exploratory Data Analysis (EDA):\n",
            "   - Distribution plots: Height (purple), Weight (pink), AST (red), TRB (purple).\n",
            "   - Pairplot of numerical features.\n",
            "   - Correlation heatmap between features.\n",
            "4. Descriptive Statistics:\n",
            "   - Summary statistics for numerical columns.\n",
            "5. Model Training:\n",
            "   - Linear Regression model trained using height, weight, AST, and TRB.\n",
            "6. Model Evaluation:\n",
            "   - Mean Squared Error (MSE): 58415.20\n",
            "   - R-squared (R2): 0.76\n",
            "7. Example Prediction:\n",
            "   - Predicted Points for example input: 154.94\n",
            "8. Saved Outputs:\n",
            "   - Predicted values saved to 'predictions.csv'.\n",
            "   - EDA plots saved as images.\n",
            "   - Actual vs Predicted PTS plot saved as 'actual_vs_predicted.png'.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ByNwqYoL_sll"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save informative summary as CSV"
      ],
      "metadata": {
        "id": "hFrNM8N__swF"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_data = {\n",
        "    'Information': [\n",
        "        \"Dataset loading: Players and Seasons_Stats datasets loaded.\",\n",
        "        f\"Players Dataset: Shape {players_df.shape}\",\n",
        "        f\"Seasons Stats Dataset: Shape {seasons_df.shape}\",\n",
        "        \"Model Evaluation:\",\n",
        "        f\"Mean Squared Error (MSE): {mse:.2f}\",\n",
        "        f\"R-squared (R2): {r2:.2f}\",\n",
        "        f\"Predicted Points for example input: {predicted_points[0]:.2f}\",\n",
        "        \"Saved Outputs:\",\n",
        "        \"Predicted values saved to 'predictions.csv'.\",\n",
        "        \"EDA plots saved as images.\",\n",
        "        \"Actual vs Predicted PTS plot saved as 'actual_vs_predicted.png'.\"\n",
        "    ]\n",
        "}"
      ],
      "metadata": {
        "id": "uDoxPUjk_pVo"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QvYYEywx_1kH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_df = pd.DataFrame(summary_data)  # Create DataFrame for summary"
      ],
      "metadata": {
        "id": "Lej3DEoY_1nI"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_df.to_csv('informative_summary.csv', index=False)  # Save summary to CSV file"
      ],
      "metadata": {
        "id": "mq_EUFW7_1p2"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"   Informative summary saved to 'informative_summary.csv'.\")  # Print message indicating file save"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phuEPdzK_1sY",
        "outputId": "6d07beee-af75-48f6-91eb-555b5327df1c"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Informative summary saved to 'informative_summary.csv'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DX9gbZNW__Np"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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        "<font color='blue'><b> Concluding Notes:</b></font>\n",
        "\n",
        "<font color='purple'><b> Project Overview:</b></font>\n",
        "- <font color='green'><b>Title:</b></font> Basketball Player Performance Prediction\n",
        "- <font color='green'><b>Objective:</b></font> Predict the performance of basketball players using machine learning techniques.\n",
        "- <font color='green'><b>Algorithm Used:</b></font> Linear Regression\n",
        "- <font color='green'><b>Dataset Source:</b></font> [Kaggle - Basketball Dataset](https://www.kaggle.com/drgilermo/nba-players-stats)\n",
        "\n",
        "<font color='purple'><b> Key Steps Covered:</b></font>\n",
        "1. <font color='blue'><b>Importing Libraries:</b></font> Essential libraries such as pandas, numpy, matplotlib, seaborn for data handling, analysis, and visualization.\n",
        "2. <font color='blue'><b>Loading Datasets:</b></font> Loaded and explored datasets (Players.csv, Seasons_Stats.csv) to understand player attributes and performance statistics.\n",
        "3. <font color='blue'><b>Data Preprocessing:</b></font> Cleaned and merged datasets, handled missing values, and prepared data for analysis.\n",
        "4. <font color='blue'><b>Exploratory Data Analysis (EDA):</b></font> Visualized distributions of player height, weight, assists, rebounds, and explored correlations between features.\n",
        "5. <font color='blue'><b>Descriptive Statistics:</b></font> Computed summary statistics to gain insights into the numerical features of the dataset.\n",
        "6. <font color='blue'><b>Handling Special Characters:</b></font> Preprocessed column names to remove special characters for better compatibility.\n",
        "7. <font color='blue'><b>Model Training:</b></font> Utilized Linear Regression to train a model on features like height, weight, assists, rebounds to predict points scored (PTS).\n",
        "8. <font color='blue'><b>Model Evaluation:</b></font> Evaluated model performance using metrics like Mean Squared Error (MSE) and R-squared (R2).\n",
        "9. <font color='blue'><b>Saving Predictions:</b></font> Saved predicted values and actual results to a CSV file ('predictions.csv') for further analysis.\n",
        "10. <font color='blue'><b>Example Prediction:</b></font> Demonstrated how to make predictions for a hypothetical player based on input features.\n",
        "11. <font color='blue'><b>Plotting Predicted vs Actual Values:</b></font> Visualized model predictions versus actual values to assess model accuracy.\n",
        "\n",
        "<font color='purple'><b> Educational Insights:</b></font>\n",
        "- <font color='green'><b>Learning ML Concepts:</b></font> Students can grasp fundamental machine learning concepts such as data preprocessing, model training, evaluation, and visualization.\n",
        "- <font color='green'><b>Practical Application:</b></font> Real-world application of ML in sports analytics, showcasing how data-driven insights can enhance decision-making.\n",
        "- <font color='green'><b>Data Visualization:</b></font> Importance of data visualization in understanding trends, correlations, and model predictions.\n",
        "- <font color='green'><b>Hands-on Experience:</b></font> Hands-on project fosters practical skills in data handling, analysis, and interpretation.\n",
        "\n",
        "<font color='purple'><b> Conclusion:</b></font>\n",
        "This project provides a comprehensive introduction to machine learning through the lens of predicting basketball player performance. By following the outlined steps and notes, students can gain valuable insights into data science workflows, model building, and evaluation techniques. The combination of theory and practical implementation equips learners with essential skills for tackling real-world data challenges."
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