This repository contains my completed projects for the Data Analysis with Python course by freeCodeCamp I've completed with the certification.
- Data Manipulation:
Python,Pandas,NumPy - Data Visualization:
Matplotlib,Seaborn - Statistical Analysis:
SciPy
Transforms a list of 9 numbers into a 3×3 matrix and computes the mean, variance, standard deviation, max, min, and sum across rows, columns, and the entire matrix. Built with NumPy and designed with strict input validation.
This project analyzes demographic data from the U.S. Census dataset using Pandas to calculate and extract insights:
- Number of individuals in each race category.
- Average age of men.
- Percentage of people with a Bachelor's degree.
- Income statistics based on education level.
- Minimum weekly work hours and the percentage of high earners among them.
- Country with the highest percentage of people earning >50K.
- Most common occupations in India.
Processes and visualizes a dataset of medical examinations. Calculates BMI to classify patients as overweight, normalizes cholesterol and glucose data, and produces two comprehensive visualizations to find correlations:
- Categorical Plot – compares health indicators between patients with and without cardiovascular disease.
- Heatmap – shows correlations between medical variables after filtering outliers.
Results:
Categorical Plot:
Correlation Heatmap:
Analyzes daily page views on the freeCodeCamp forum from May 2016 to December 2019. The dataset was cleaned by removing the top and bottom 2.5% of page views to eliminate extreme outliers. The date column was converted into a datetime index for time-series handling.
Visualizations generated to highlight long-term trends and seasonal patterns:
- Line Plot - shows overall page views over time and highlights the long-term upward/downward trends.
- Bar Plot - displays the average monthly page views for each year and helps compare year-to-year performance.
- Box Plots - year-wise box plot shows how the distribution of page views changes over years, and month-wise box plot reveals seasonal patterns across different months.
Results:
Line Plot:
Box Plot:
Bar Plot:
Analyzes historical datasets of global average sea level changes since 1880. This project uses Pandas to process the data and Matplotlib with SciPy to visualize and predict future sea levels.
The script creates a scatter plot of historical data and uses scipy.stats.linregress to calculate:
- A line of best fit for the entire dataset (1880 - present) extending to the year 2050.
- A second line of best fit using only recent data (from the year 2000 onwards) to predict a more accurate rate of sea level rise by 2050.
Results:
Sea Level Prediction Plot:
