7+ Python Project Ideas for your Data Science Resume

Python has become a celebrated language in data science, admired for its simplicity and extensive libraries. It is a preferred choice for beginners, offering easy readability and minimal syntax. Popular libraries include seaborn, matplotlib, and sci-kit learn. A curated list of Python projects facilitates hands-on learning, making it an ideal starting point for aspiring data scientists.

‍

1. Squid games sentiment analysis 

Project Aim:  Python project developed for sentiment analysis to understand people's future reactions to Squid Game.

Description: Squid Game is a super popular Netflix show about important stuff like fairness and greed. People worldwide love talking about it online, making it a big cultural deal. Utilizing Natural Language Processing NLP and Python libraries,  in this project assess social media posts to identify whether sentiments are positive, negative, or neutral. Sentiment analysis aims to offer valuable insights into the future themes, cultural impact, and success of the show.

Level: Beginner

What you’ll learn: 

• Text pre-processing
• Tokenization 
• Imbalanced data handling
• Text Classification

Tools  & Libraries : 

• Python
• NLTK Library 
• Pandas, seaborn and matplotlib for data visualization and manipulation

‍

2. Password Strength Checker with Machine Learning

Project Aim: The aim is to develop a  classification tool that can assess and categorize passwords based on their strength.

Description: A Password Strength Checker is like a tool that tells you how strong your password is. Some of these tools use smart computer programs to guess how secure your password is. In this project use machine learning  model on a labeled dataset to distinguish strong and weak passwords.

Level: Beginner

What you’ll learn: 

• Text feature extraction using Tfidf Vectorizer to tokenize each and every character
• ML Model training on text data 

Tools  & Libraries : 

• Pandas, seaborn and matplotlib for data manipulation & visualization
• Scikit-learn for ML 

‍

3. World Population 2023-Exploratory Data Analysis 

Project Aim: The goal is to gain insights, patterns, and trends in the data through visualizations and statistical analysis.

Description: The project involves Exploratory Data Analysis (EDA) on the world population data from the year 2023 using Python. Through visualizations and statistical analysis, the aim is  to uncover patterns, trends, and insights within the dataset, providing a comprehensive understanding of global population dynamics during that specific year.

Level: Beginner

What you’ll learn: 

•  Data preparation and cleaning  to enhance the quality of dataset such as removing duplicate columns, sorting and merging data columns.
• Typecasting for converting datatypes of columns into relevant datatypes
• Statistical analysis and  deriving insights from visualizations. 

Tools  & Libraries : 

• Pandas for data manipulation and analysis.
• Matplotlib and Seaborn for data visualization. 
• Plotly for  interactive visualizations
• Jupyter Notebooks for conducting EDA

‍

4. Detection of forest fires 

Project Aim: Aim is to develop a machine learning model for predicting forest fires in order to enhance early detection and aid in effective resource allocation.

Description:

Developing a project to detect forest fires is a great way to showcase Data Science skills. Forest fires, also known as wildfires, are uncontrolled fires in forests causing extensive damage to habitats, the environment, and property. Utilizing k-means clustering helps pinpoint critical hotspots, reducing intensity, managing, and predicting fire behavior. This aids in efficient resource allocation. Enhancing model accuracy involves incorporating climatological data to identify prevalent times and seasons for wildfires.

Level: Beginner

What you’ll learn:

• cleaning and preparing datasets, dealing with missing values, and assuring data quality for model training
• Feature selection techniques
• Classification techniques 

Tools  & Libraries : 

• Pandas and NumPy
• Scikit learn for Machine Learning
• Matplotlib for EDA

‍

5. Classifying Song Genres from Audio Data

Project Aim: Using Machine Learning techniques to categorize song genres from audio data for tailored music recommendations.

Description: Classifying song genres from audio data is a fascinating project that involves using machine learning techniques to analyze and categorize music based on its audio features. The primary goal is to develop a model that can automatically assign a genre label to a given song based on its audio characteristics.

Level: Intermediate 

What you’ll learn:

• Learning to handle Json files.
• Audio feature extraction
• Data exploration
•  Interpreting the decisions of the trained model, understanding the features that contribute to genre classification.

Tools  & Libraries : 

• JSON Library
• Librosa for audio feature extraction 
• Pandas, NumPy, Matplotlib, Scikit Learn 

‍

6. Ola Bike Ride Request Demand Forecast 

Project Aim: Predict  taxi ride demand in a specific area using prediction algorithms. 

Description: Meeting ride requests is challenging due to their unpredictable and spontaneous nature. Hence, it's crucial to implement a prediction algorithm to estimate the upcoming ride demand. This project focuses on forecasting ride-request demands in a specific area, identified by latitude and longitude values, over a defined duration in military hours.

Level: Intermediate 

What you’ll learn:

• Feature engineering to derive important features.
• Finding relationship between features using correlation and heatmaps and univariate and bivariate analysis.
• Regression analysis 

Tools  & Libraries : 

• Pandas and NumPy
• Matplotlib& Seaborn
• Scikit-learn
• XG Boost 

‍

7. Loan Approval Prediction 

Project Aim: Predict loan approval using machine learning based on historical data and applicant information.

Description:

The project involves loan prediction through a machine learning approach. Traditional loan approval considers factors like credit score, loan amount, lifestyle, career, and assets. This project leverages machine learning algorithms to analyze historical data of past applicants, identifying patterns in their loan repayment behavior. By examining diverse datasets, the aim is to create a model that can predict the likelihood of loan approval for new applicants based on similar criteria and historical trends.

Level: Intermediate 

What you’ll learn: 

• Data preprocessing and cleaning for handling missing values and outlier detection
• EDA to find relationship between different variables
• Feature selection techniques 
• Model evaluation techniques 

Tools  & Libraries : 

• Pandas:  To load the Data Frame
• Matplotlib: To visualize the data features i.e. Bar Plot
• Seaborn: To see the correlation between features using heatmap
• Scikit  learn for machine learning 

‍

8. Stock Market Performance Analysis

Project Aim: Creating a Stock Price Performance Analysis  System through the application of Deep Learning algorithms for precise forecasting of stock prices.

Description : This project involves looking at past stock information and using different data techniques to predict what future stock prices might be. We clean up the data, create useful features, and apply deep learning tools like Recurrent neural network, and Long Short Term Memory(LSTM). The goal is to make a model that can give insights into where the market might be heading, helping investors make smarter decisions.

Level:  Advanced 

What you’ll learn :

• Time series analysis using deep learning
• Correlation analysis 
• Hyper-paramter optimization of  deep learning model 
• EDA and Moving Average Analysis 

Tools & Libraries :

• Python 
• NumPy & Pandas for data manipulation 
• Matplotlib and Seaborn for EDA
• TensorFlow or PyTorch as  deep learning frameworks

‍

9. Friends and Family Face Recognition 

Project Aim: The project's aim is to empower users to build a personalized face recognition tool using Python, fostering skills in computer vision.

Description: Create a face recognition tool using Python. Learn face detection and recognition to identify faces in images, exploring the technology behind phone face unlock features. You can collect your own dataset by taking images from your phone or work with the dataset provided here. 

Level: Advanced 

What you’ll learn: 

• Develop datasets for face recognition
• Utilize Face_Recognition for face detection
•  Generate face encodings from detected images
•  Recognize known faces in unknown images
• Implement argparse for a command-line interface
• Utilize Pillow to draw bounding boxes

Tools  & Libraries : 

• Installing external modules using pip
• argparse for crafting a command-line interface
• Accessing and reading files using pathlib
•  Serializing  and deserializing Python objects using pickle

‍

10. Recognition of Traffic Signals

Project Aim: Build a system for recognizing traffic signs using Deep Learning algorithms, aiming to enhance road safety and improve traffic management.

Description: Following traffic signs is super important to avoid accidents. Before getting a driver's license, you need to learn what each sign looks like. In the Traffic Signs Recognition project, you have to  use a computer program to figure out what type of traffic sign is in a picture. You can train the program using a dataset and create a simple interface using Python. This project also looks ahead to a future with more automated vehicles.

Level: Advanced

What you’ll learn: 

• GUI development
• How to process and analyze images for object recognition
• Data preprocessing and management using pandas and numpy. 

Tools  & Libraries : 

• Tkinter GUI development library 
• TensorFlow or PyTorch library  for deep learning 
• OpenCV for computer vision tasks
• Pandas and NumPy