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https://github.com/anidipta/dose-of-data


https://github.com/anidipta/dose-of-data

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README 

        



# **🔍 Pokémon Data Analysis**



![Pokemon](https://media.licdn.com/dms/image/D5612AQHcM0VMFJgApQ/article-cover_image-shrink_720_1280/0/1693159389529?e=1723075200&v=beta&t=Zyzi7Gmq52ZgIBws46Fb-1D9rAJLLw1HjktPqWuoRj0)



## **📚 Overview**



The **Pokémon** dataset encompasses a variety of attributes for each Pokémon, including names, types, base stats, abilities, and more. In this analysis, our objectives are:



- **🔍 Conduct Comprehensive Exploratory Data Analysis (EDA)**: Visualize the distributions of Pokémon attributes, uncover patterns, and identify correlations within the data.

- **🧠 Develop Predictive Models**: Use classification algorithms to accurately categorize Pokémon as legendary or non-legendary based on their attributes.

- **📊 Present Insights Effectively**: Showcase the findings with detailed visualizations, insightful descriptions, and thorough documentation.



## **📊 Dataset**



The **Pokémon dataset** contains detailed information about various Pokémon species, including their attributes, abilities, and classifications.  

📥 Dataset --> [Data](pokemon.csv)



📜 Attributes



|    Field Name                   |   Description                                                                 |

|-----------------------|----------------------------------------------------------------------------|

| **💡 abilities**| A list of abilities possessed by each Pokémon.|

| **⚔️ against_**| Attributes representing the effectiveness of each Pokémon type against other types (e.g., against_bug, against_dark).|

| **⚔️ attack**| Base attack stat of the Pokémon.|

| **🛡️ defense**| Base defense stat of the Pokémon.|

| **🔮 sp_attack**| Base special attack stat of the Pokémon.|

| **🔮 sp_defense**| Base special defense stat of the Pokémon.|

| **🏃 speed**| Base speed stat of the Pokémon.|

| **⚖️ weight_kg**|Weight of the Pokémon in kilograms.|

| **📏 height_m**| Height of the Pokémon in meters.|

| **🏆 base_total**| The total base stats of the Pokémon.|

| **🎯 capture_rate**| Capture rate of the Pokémon.|

| **🏅 classification**| Classification of the Pokémon.|

| **🔄 experience_growth**| Experience growth rate of the Pokémon.|

| **📆 generation**| Generation in which the Pokémon was introduced.|

| **⭐ is_legendary**| Binary attribute indicating whether the Pokémon is legendary (1) or not (0).|

| **🇯🇵 japanese_name**| Japanese name of the Pokémon.|

| **🔠 name**| English name of the Pokémon.|

| **♂️ percentage_male**| Percentage of male Pokémon in the species.|

| **🔢 pokedex_number**| Pokédex number of the Pokémon.|

| **🔥 type1**| Primary type of the Pokémon.|

| **💧 type2**| Secondary type of the Pokémon.|



## 🎯 Objectives



1. **📈 Explore the Dataset**: Analyze Pokémon attributes, types, and generations.

2. **🔍 Perform EDA**: Visualize distributions and identify correlations.

3. **🧠 Build Models**: Develop algorithms to classify Pokémon as legendary or non-legendary.

4. **🔎 Analyze Patterns**: Uncover trends and insights in Pokémon data.

5. **📊 Present Findings**: Provide clear visualizations and comprehensive documentation.



## **📂 Contents**



- Dataset - [Data](pokemon.csv)

- Jupyter Notebook - [.ipynb file](pokemon_anidipta.ipynb)

- Dependencies List - [List](requirements.txt)

- Documentation - [File](README.md)



## 🛠️ Methodology



1. **📥 Data Loading**: Load the Pokémon dataset.

2. **🧹 Data Cleaning and Wrangling**: Handle missing values, correct data types, and remove duplicates to ensure data integrity and accuracy.

3. **📊 Exploratory Data Analysis (EDA)**: Visualize distributions, correlations, and patterns in the data using Python libraries like Matplotlib, Squarify, and Seaborn.

4. **🛠️ Data Preprocessing**: Normalize features, encode categorical variables, and split the dataset into training and testing sets for model training.

5. **🧠 Predictive Modeling**: Build and evaluate machine learning models using Scikit-learn to classify Pokémon as legendary or non-legendary.



## **▶️ How to run**



To run the analysis notebook locally, follow these steps:



1. 📂 Clone this repository to your local machine.

2. 📦 Install Dependencies --> run `pip install -r requirements.txt`.

3. 📝 Open the Jupyter Notebook (`pokemon_anidipta.ipynb`).

4. 🏃 Execute the Code.



## 🏁 Conclusion



The **Pokémon Dataset Analysis** project provides deep insights into Pokémon characteristics and classifications. These findings are valuable for enthusiasts, researchers, and anyone interested in data analysis and machine learning.