Python data analysis projects for final year

Eve 23 Published: 02/04/2025

Python data analysis projects for final year

I'm happy to help you with that! Here are some exciting Python data analysis project ideas suitable for your final year:

COVID-19 Data Analysis: Utilize publicly available datasets from reputable sources like the World Health Organization (WHO), Johns Hopkins University, or the Centers for Disease Control and Prevention (CDC). Analyze trends in confirmed cases, deaths, recoveries, and vaccination rates across different countries or regions. You can also explore demographic factors that influence infection rates. Movie Recommendation System: Using datasets from platforms like IMDB, Rotten Tomatoes, or MovieLens, create a recommendation system based on user ratings and reviews. Develop a clustering algorithm to group similar movies together and suggest new films to users based on their viewing history. Stock Market Analysis: Collect historical stock prices for different companies (e.g., Apple, Amazon, Google) using APIs from reputable sources like Yahoo Finance or Quandl. Analyze trends in price movements, volume, and technical indicators like Moving Averages (MA), Relative Strength Index (RSI), and Stochastic Oscillator. Customer Segmentation: Use datasets from e-commerce platforms like Amazon or eBay to identify customer segments based on demographics, purchase history, and shopping behavior. Develop a clustering algorithm to group similar customers together and predict future purchasing patterns. Weather Forecasting: Utilize weather-related datasets from sources like the National Weather Service (NWS), NASA, or the European Centre for Medium-Range Weather Forecasts (ECMWF). Analyze trends in temperature, humidity, wind speed, and precipitation rates to develop a predictive model for short-term weather forecasting. Music Genre Classification: Collect audio features for songs from datasets like MagnaTagATune, Million Song Dataset, or Spotify's API. Develop a classification algorithm to classify songs into different genres (e.g., rock, pop, jazz) based on characteristics like tempo, pitch, and timbre. Traffic Pattern Analysis: Utilize traffic data from sources like the Federal Highway Administration, the U.S. Census Bureau, or urban planning organizations. Analyze trends in traffic volume, speed, and congestion patterns to develop a predictive model for traffic flow and identify areas for improvement. Sports Performance Analytics: Collect statistics for professional sports teams (e.g., football, basketball, baseball) from sources like ESPN, Sports Illustrated, or the Official League Websites. Develop a regression analysis to predict team performance based on factors like player statistics, game conditions, and team dynamics. Healthcare Insurance Analysis: Use datasets from healthcare organizations, insurance providers, or government agencies (e.g., CMS, Medicaid) to analyze trends in healthcare utilization rates, costs, and patient outcomes. Develop a predictive model for disease prevalence based on demographic and behavioral factors. Election Prediction: Collect data on past election results, voter demographics, and campaign spending from sources like the National Archives, the Federal Election Commission, or political news outlets. Develop a regression analysis to predict electoral outcomes based on historical trends and current events.

These projects offer a range of exciting opportunities for you to apply your Python skills to real-world datasets. Remember to focus on a specific aspect that interests you and to explore different aspects of data analysis, such as visualization, machine learning, or statistical modeling!

Python data analysis projects with source code

I'll respond in English this time! Here are some exciting Python data analysis projects with source code:

1. Analyzing Movie Data

In this project, we'll analyze a dataset of movies from IMDB. We'll explore the distribution of ratings and genres, identify popular directors and actors, and visualize the relationships between them.

import pandas as pd

import matplotlib.pyplot as plt

Load movie data

movie_data = pd.read_csv('movies.csv')

Explore the distribution of ratings and genres

print(movie_data['rating'].describe())

print(movie_data['genre'].value_counts())

Identify popular directors and actors

popular_directors = movie_data['director'].value_counts().head(10)

popular_actors = movie_data['actor'].value_counts().head(10)

Visualize the relationships between directors and actors

plt.figure(figsize=(8, 6))

sns.heatmap(movie_data.groupby('director')['actor'].count().unstack().fillna(0), cmap='Blues')

plt.xlabel('Actor')

plt.ylabel('Director')

2. Analyzing COVID-19 Data

In this project, we'll analyze a dataset of COVID-19 cases from the World Health Organization (WHO). We'll explore the trends in new cases and deaths over time, identify high-risk countries, and visualize the global spread of the pandemic.

import pandas as pd

import matplotlib.pyplot as plt

Load COVID-19 data

covid_data = pd.read_csv('covid_data.csv')

Explore the trends in new cases and deaths over time

print(covid_data['date'].describe())

print(covid_data.groupby('date')['cases'].sum().plot())

Identify high-risk countries

high_risk_countries = covid_data.groupby('country')['cases'].sum().sort_values(ascending=False).head(10)

Visualize the global spread of COVID-19

plt.figure(figsize=(8, 6))

sns.scatterplot(x='latitude', y='longitude', data=covid_data)

3. Analyzing Customer Purchase Data

In this project, we'll analyze a dataset of customer purchases from an e-commerce website. We'll explore the distribution of purchase amounts and frequencies, identify high-value customers, and visualize the relationships between products and customers.

import pandas as pd

import matplotlib.pyplot as plt

Load customer purchase data

purchase_data = pd.read_csv('purchases.csv')

Explore the distribution of purchase amounts and frequencies

print(purchase_data['amount'].describe())

print(purchase_data.groupby('customer')['amount'].sum().plot(kind='bar'))

Identify high-value customers

high_value_customers = purchase_data.groupby('customer')['amount'].sum().sort_values(ascending=False).head(10)

Visualize the relationships between products and customers

plt.figure(figsize=(8, 6))

sns.heatmap(purchase_data.pivot_table(index='product', columns='customer', values='amount').fillna(0), cmap='Blues')

These projects demonstrate some of the many exciting ways you can use Python for data analysis. You can customize these projects to fit your specific interests and goals, and I hope they inspire you to explore even more!