🚀 Data Analysis Odyssey

A journey through data, one week at a time.

Welcome to my Data Analysis Odyssey! This repository chronicles my two-month adventure in exploring the vast universe of Python-based data analysis, visualization, and modeling. From mastering the basics to conquering advanced techniques, this project captures my growth, learnings, and accomplishments week by week.

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🌟 Project Highlights

🔍 Data Exploration: Unlocking the stories hidden within raw data.
📊 Visual Masterpieces: Turning numbers into art with charts and plots.
🧠 Feature Engineering: Crafting insights for smarter models.
📚 Advanced Techniques: From time series forecasting to sentiment analysis and clustering.

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🗂️ Table of Contents

1. Getting Started
2. Weekly Objectives
3. Tools & Technologies
4. How to Use
5. Repository Structure
6. Acknowledgments

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🎯 Weekly Objectives

Week 1 (June 5 – June 11): Python Fundamentals

• 🛠️ Create and manipulate lists, dictionaries, and sets.
• ✨ Perform operations like adding, removing, and modifying elements.

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Week 2 (June 12 – June 18): Data Manipulation with Pandas

• 📥 Load datasets into DataFrames.
• 🔎 Filter, clean, and summarize data.

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Week 3 (June 19 – June 25): Data Visualization Magic

• 📊 Create bar and line charts with Matplotlib.
• 🎨 Customize visuals with titles, labels, and legends.

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Week 4 (June 26 – July 2): Exploratory Data Analysis (EDA)

• 🔍 Uncover data patterns with distributions and correlations.
• 🚨 Identify and visualize outliers.

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Week 5 (July 3 – July 9): Feature Engineering & Selection

• 🧩 Engineer new features to enhance datasets.
• 📉 Use PCA and feature importance to select the best attributes.

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Week 6 (July 10 – July 16): Time Series Forecasting

• 📈 Detect trends and seasonality using time series analysis.

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Week 7 (July 17 – July 23): Sentiment Analysis & Text Mining

• 💬 Analyze unstructured data for sentiment and insights.

Week 8 (July 24 – July 31): Clustering & Classification

• 🌀 Segment data with clustering.
• 🧠 Apply classification techniques for pattern recognition.

Week 9 (August 1 – August 5): Final Touches

• 📝 Document findings and create stunning reports.

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🛠️ Tools & Technologies

• Languages: Python
• Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn, NLTK
• Visualization: Seaborn, Matplotlib
• Machine Learning: Scikit-learn

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🧑‍💻 How to Use

1. Clone the Repository:

   git clone https://github.com/your-username/data-analysis-odyssey.git  
   cd data-analysis-odyssey  

2. Install Dependencies:

   pip install -r requirements.txt  

3. Run the Code:
   Navigate to the corresponding week's folder and execute the scripts.

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📂 Repository Structure

data-analysis-odyssey/  
├── week1_basics/  
├── week2_data_manipulation/  
├── week3_visualization/  
├── week4_eda/  
├── week5_feature_engineering/  
├── week6_time_series/  
├── week7_sentiment_analysis/  
├── week8_clustering_classification/  
├── week9_final_review/  
├── README.md  
└── requirements.txt  

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❤️ Acknowledgments

This project reflects my growth as a data enthusiast. Thanks to my mentors, peers, and the open-source community for their support and inspiration throughout this journey.

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🌟 Let’s unravel the mysteries of data together!
👾 Feel free to explore, fork, and contribute to this project.

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