ML Journey

This repository documents my learning journey in Data Science and Machine Learning using Python.

Topics Covered

• Python Fundamentals
• NumPy
• Pandas
• Data Visualization
• Data Cleaning
• Exploratory Data Analysis (EDA)

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Repository Structure

ML-JOURNEY/
│
├── datasets/
├── notebooks/
├── projects/
├── python_practice/
└── README.md

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Notebooks Included

Notebook	Description
01_numpy_fundamentals.ipynb	NumPy basics and array operations
02_pandas_reading_files.ipynb	Reading CSV, Excel, JSON, and TXT files
03_pandas_filtering_ordering.ipynb	Filtering and sorting data
04_pandas_indexing.ipynb	Indexing and selecting data
05_pandas_groupby_aggregation.ipynb	GroupBy and aggregation operations
06_pandas_merge_join_concat.ipynb	Merge, join, and concatenate operations
07_data_visualization.ipynb	Data visualization using Python
08_data_cleaning.ipynb	Data cleaning techniques
09_exploratory_data_analysis.ipynb	Exploratory Data Analysis

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Projects

• Rock Paper Scissors Game

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Tech Stack

• Python
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook
• VS Code

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Current Learning Roadmap

• Python Fundamentals
• NumPy
• Pandas
• Data Visualization
• Data Cleaning
• Exploratory Data Analysis

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Goal

To build strong foundations in:

• Data Analysis
• Machine Learning
• Feature Engineering
• Statistical Thinking
• Real-world ML Projects