zarifshawon/getting-started-with-data-analysis-with-python

This Jupyter notebook provides a step-by-step guide for data analysis using Python and Jupyter Notebook.

Getting-started-with-data-analysis-with-python

This Jupyter notebook provides a step-by-step guide for data analysis using Python and Jupyter Notebook.

The Jupyter notebook provides a step-by-step guide for data analysis using Python and Jupyter Notebook. Here's a summary of the main tasks covered:

1. Task 1: Setting up the Environment

   • Install required libraries mentioned in the instructions.

2. Task 2: Using the pandas Python Library

   • Download NBA data from a specified URL using the requests library.
   • Utilize the pandas library to load the data into a DataFrame and perform initial data exploration (e.g., checking data types, displaying summary statistics).

3. Task 3: Get to Know Your Data

   • Explore data types and basic statistics using the .info() and .describe() functions.
   • Answer specific questions about the dataset, such as the number of wins and losses for a particular team.

4. Task 4: Data Access methods (loc and iloc)

   • Demonstrate data access methods (loc and iloc) for retrieving specific rows and columns from the DataFrame.

5. Task 5: Querying the Dataset

   • Filter and query the dataset based on specific conditions using boolean indexing.
   • Answer questions related to playoffs games and points scored by teams in specific years.

6. Task 6: Grouping and Aggregating your Data

   • Use grouping and aggregation functions to analyze data based on specific criteria (e.g., total points scored by teams).

7. Task 7: Manipulating Columns

   • Add, rename, and drop columns in the DataFrame.

8. Task 8: Specifying Data Types

   • Convert data types for improved performance, such as changing date columns to datetime and categorical data type for specific columns.

9. Task 9: Cleaning the Data

   • Address missing values, invalid values, and inconsistent values in the dataset.

10. Task 10: Data Visualization

    • Utilize matplotlib and seaborn for data visualization, including line plots and bar plots.
    • Answer questions about team performance based on visualizations.

11. Task 11: Introduction to Scikit Learn

    • Introduce the scikit-learn library for machine learning.
    • Calculate and visualize the correlation matrix.
    • Apply logistic regression for prediction and evaluate the model's accuracy.

The notebook provides a comprehensive guide for data analysis, including data cleaning, exploration, visualization, and an introduction to machine learning using logistic regression.