10 Real-World Projects That Will Get You A Data Scientist Job (with source code)

Practice and Put these real world projects in your Data Science Portfolio and see the magic happen.

Data Science is undoubtably the most sort after skill in 2024 and beyond. Every company is trying to beat the A.I competition and they are seeking after Data Scientist who have the proven skill to help them stay in the competition. In as much as there has been more openings for Data Scientist positions recently (according to the U.S labor of statistics data), no company will blindly hire someone without a proven skill to pay them big checks.

Many Data Science enthusiasts, especially beginners and those looking to make career transitions find it very difficult to find real-world data science hands-on project that is up to industry standard and can speak volume on in their portfolio and resume. This is the main reason why Bethel Labs has put together these real-world projects that Data Scientist can learn from and demonstrate to recruiters of their experience in Data Science.

Note: these projects are created with data from real world scenarios, some of them connecting to live APIs to ingest data from source providers, which is what you need in real life case studied

Let’s get started…

Project 1

Title : Building & Deploying Real World Movie Recommendation Systems

Demo

NB: Before playing the demo below, make sure to select Settings > Quality > choose the highest quality (e.g 1080p)

source: https://youtu.be/Ao3DrwNFXgY

Project files and source code

Problem Statement:

In the era of digital streaming, viewers are inundated with countless movie choices across various platforms. This often leads to decision fatigue, where users struggle to choose what to watch next. A personalized movie recommendation system is needed to help users discover movies that align with their preferences. Furthermore, integrating a user authentication system will allow for personalized experiences, where recommendations and preferences are saved for each individual user.

This project aims to develop a comprehensive Movie Recommendation System that offers personalized movie recommendations using both content-based and collaborative filtering techniques. The system will also include a user authentication feature that allows users to log in, register, and access personalized recommendations based on their unique preferences.

Key Features:

Movie Recommendation System:

• Content-Based Filtering: Recommends movies similar to the ones a user has liked based on genres, descriptions, and other movie metadata.
• Collaborative Filtering: Suggests movies based on what other users with similar tastes have liked.
• Hybrid Model: Combines both content-based and collaborative filtering to improve recommendation accuracy.

1. User Authentication System:

• Login/Registration: Users can create an account or log in to an existing account to access personalized recommendations.
• Session Management: Tracks user sessions to ensure that each user’s data and preferences are kept separate and secure.

Movie Details Page:

• Genre Display: Shows movie genres in a clean, user-friendly format.
• Top Billed Cast: Displays headshots and names of top cast members.
• Trailer Integration: Includes a movie trailer in the background for an immersive experience.

Search and Discovery:

• Search Functionality: Allows users to search for specific movies.
• Trending Movies: Displays currently popular movies.
• Top 10 Movies: Lists the top 10 movies in the user’s region.

User Interface:

• Dynamic and Responsive Design: Ensures that the application is user-friendly and accessible across different devices.

Software and Tools Used:

Programming Language:

• Python: Core programming language used for backend development.

Web Framework:

• Flask: Lightweight web framework used to develop the backend of the application, manage routes, and handle server requests.

Database Management:

• SQLite (or an alternative database system): Used to store user credentials, movie data, and user preferences.

API Integration:

• TMDb API: Fetches movie data, including details, ratings, and recommendations.

Machine Learning Libraries:

• Scikit-Learn: Used for implementing content-based and collaborative filtering models.
• Pandas: For data manipulation and analysis.
• Pickle: For saving and loading machine learning models.

Frontend Technologies:

• HTML/CSS: Used for designing the layout and styling of the web pages.
• JavaScript: Provides dynamic functionalities like search suggestions and page interactions.
• Bootstrap (optional): To ensure responsive design and consistency across different devices.

Techniques Applied:

Content-Based Filtering:

• Uses TF-IDF Vectorization and Cosine Similarity to recommend movies based on the content.

Collaborative Filtering:

• Employs K-Nearest Neighbors (KNN) to recommend movies based on user behavior and similarities between users.

Hybrid Recommendation System:

• Combines the strengths of content-based and collaborative filtering to provide more accurate recommendations.

User Authentication and Session Management:

• Implements secure login and registration functionalities to personalize the user experience.

Skills Applied and Learned:

1. Backend Development:

• Gained proficiency in Flask, handling routes, and integrating APIs.

Machine Learning:

• Applied machine learning techniques for recommendation systems.
• Enhanced understanding of content-based filtering, collaborative filtering, and hybrid models.

Web Development:

• Developed skills in HTML, CSS, and JavaScript for frontend design.
• Learned to create responsive and dynamic user interfaces.

API Integration:

• Practiced integrating external APIs to fetch and utilize data in the application.

Data Management:

• Improved skills in data manipulation with Pandas and managing databases

User Experience (UX) Design:

• Focused on creating a user-friendly interface that is both intuitive and visually appealing.

Project Management:

• Gained experience in managing a full-stack development project, from planning and design to implementation and testing.

This project showcases a comprehensive approach to solving a common problem in the entertainment industry, using a combination of machine learning, web development, and user experience design.

Project 2

Title : Development of an Advanced E-commerce Recommendation System

Demo

source : https://youtu.be/BS_ZpiJgmNA

Project files and source code

Problem Statement & Objective

The primary objective of this project is to design and develop a comprehensive, industry-standard e-commerce recommendation system that emulates the sophisticated recommendation engines used by leading e-commerce platforms such as Amazon.com. This system aims to enhance user experience by providing personalized product recommendations, thereby increasing customer engagement and conversion rates.

Background:

In today’s competitive e-commerce landscape, personalized recommendations are crucial for retaining customers and driving sales. Traditional e-commerce platforms often struggle to present relevant products to users, leading to missed opportunities and decreased customer satisfaction. Leveraging advanced algorithms, such as collaborative filtering and content-based filtering, can significantly improve the accuracy of product recommendations.

Challenges:

1. Data Integration and Preprocessing: The recommendation system must be able to handle large volumes of diverse data, including user preferences, product descriptions, ratings, and reviews. Efficient preprocessing is necessary to clean and structure the data for analysis.
2. Algorithm Selection and Implementation: Selecting appropriate algorithms (e.g., SVD for collaborative filtering and TF-IDF for content-based filtering) that can accurately predict user preferences based on both user behavior and product features is critical. The algorithms must be implemented in a way that balances accuracy with computational efficiency.
3. Real-time Performance: The system must be capable of delivering real-time recommendations without compromising the user experience. This includes optimizing the recommendation algorithms for speed and ensuring that the web application can handle concurrent users efficiently.
4. User Interface and Experience: The recommendation system needs to be seamlessly integrated into an intuitive user interface. The design should facilitate easy navigation and ensure that recommended products are prominently displayed without overwhelming the user.
5. Scalability and Deployment: The recommendation system must be scalable to accommodate growing data and user base. It should be deployed in a robust environment using a framework like Flask, which supports easy scaling and maintenance.

Solution Approach:

1. Data Collection and Preprocessing: Utilize a comprehensive dataset containing product information, user reviews, and ratings. The data will be cleaned and transformed to ensure consistency and relevance for the recommendation algorithms.
2. Collaborative Filtering: Implement an SVD-based collaborative filtering algorithm using the Surprise library. This algorithm will predict user preferences based on past interactions with similar products and users.
3. Content-Based Filtering: Develop a content-based filtering approach using TF-IDF vectorization of product descriptions. Cosine similarity will be used to recommend products that are similar to those the user has interacted with before.
4. Hybrid Recommendation System: Combine collaborative filtering and content-based filtering to create a hybrid recommendation system. This approach will provide more accurate recommendations by leveraging both user behavior and product content.
5. Web Application Development: Develop a user-friendly web application using Flask. The application will feature a dynamic homepage showcasing best-selling products, a detailed product page with personalized recommendations, and search functionality. The design will closely mimic the user experience of leading e-commerce platforms.
6. Deployment and Testing: Deploy the application on a web server and conduct thorough testing to ensure it meets performance and scalability requirements. The system will be tested with a significant number of users to ensure real-time performance and accuracy.

Expected Outcomes:

• A fully functional e-commerce recommendation system that enhances user experience by providing personalized, accurate product recommendations.
• Improved user engagement and increased sales conversion rates due to the relevance and precision of recommendations.
• A scalable, maintainable system that can be deployed in real-world e-commerce environments.

This project will demonstrate the application of advanced machine learning techniques in solving real-world problems in e-commerce, showcasing the potential of AI-driven personalization to improve customer satisfaction and business outcomes.

Project 3

Title : Airline Booking Demand Forecasting and Dynamic Pricing Optimization App

Demo

source : https://youtu.be/BS_ZpiJgmNA

Project files and source code

Problem Statement:

Airline companies face significant challenges in managing their booking demand and pricing strategies. They need to balance the trade-off between maximizing revenue and minimizing the number of empty seats on a flight. To address this challenge, we can build a machine learning project that forecasts demand for airline bookings and optimizes prices to maximize revenue.

Dataset:

• Historical booking data: This includes information on past bookings, such as:
• Flight information (e.g., departure and arrival airports, dates, times)
• Booking dates and times
• Passenger information (e.g., age, travel class)
• Fare information (e.g., price, fare class)
• External data: This includes information on external factors that may impact demand, such as:
• Holidays and special events
• Weather forecasts
• Economic indicators (e.g., GDP, inflation rate)
• Competitor pricing data

Tasks:

1. Data Preprocessing:

• Clean and preprocess the historical booking data and external data
• Handle missing values and outliers
• Transform data into a suitable format for modeling

1. Demand Forecasting:

• Develop a machine learning model to forecast demand for airline bookings
• Use techniques such as:
• Time series analysis (e.g., ARIMA, Prophet)
• Regression analysis (e.g., linear regression, decision trees)
• Ensemble methods (e.g., random forest, gradient boosting)
• Evaluate the performance of the model using metrics such as mean absolute error (MAE) and mean squared error (MSE)

1. Price Optimization:

• Develop a machine learning model to optimize prices based on the forecasted demand
• Use techniques such as:
• Linear programming
• Dynamic pricing
• Reinforcement learning
• Evaluate the performance of the model using metrics such as revenue and profit

1. Model Deployment:

• Deploy the demand forecasting and price optimization models in a production-ready environment
• Integrate the models with the airline’s booking system
• Monitor the performance of the models and retrain as necessary

Real-World Impact:

• Improved demand forecasting: The airline can better anticipate demand and adjust their capacity accordingly, reducing the number of empty seats and increasing revenue.
• Optimized pricing: The airline can set prices that maximize revenue based on forecasted demand, improving profitability.
• Enhanced customer experience: By optimizing prices and capacity, the airline can offer more competitive fares and improve the overall customer experience.

Machine Learning Techniques:

• Time series analysis
• Regression analysis
• Ensemble methods
• Linear programming
• Dynamic pricing
• Reinforcement learning

Tools and Technologies:

• Python
• Pandas
• NumPy
• Scikit-learn

Project 4

Title : Document Segmentation and Information Extraction with Camera Input

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Project Overview:

This project focuses on developing a Streamlit-based web application that automates the extraction, segmentation, and categorization of textual information from various document types, such as images and PDFs. The goal is to provide an efficient and user-friendly tool that can handle multiple document uploads, process each document to identify and extract key textual elements, and allow users to download the extracted information in different formats.

Software and Tools Used:

• Streamlit: The primary framework used to build the web application, allowing for an interactive and responsive user interface.
• Python: The programming language used to implement the backend logic, data processing, and integration with machine learning models.
• Pytesseract: An OCR (Optical Character Recognition) engine that extracts text from images and PDFs.
• pdf2image: A Python library used to convert PDF pages into images, making them suitable for OCR processing.
• Transformers (Hugging Face): The LayoutLM model from the Hugging Face library is used to classify and segment text entities in the extracted data.
• PIL (Python Imaging Library): Used for image manipulation and drawing segmented bounding boxes around detected text entities.
• ReportLab: A library used to convert extracted text into PDF format for download.
• Openpyxl: A library used to convert extracted text into Excel format for download.
• Streamlit-webrtc: A Streamlit component used for real-time video processing to capture documents via internal or external cameras.

Techniques Applied:

• Optical Character Recognition (OCR): Leveraged pytesseract to extract text from images and PDFs, enabling automated text processing and analysis.
• Text Segmentation and Classification: Employed the LayoutLM model to segment and classify text into predefined categories (e.g., Address, Date, Total) based on custom rules.
• Image Processing: Used PIL for preprocessing images, including grayscale conversion and thresholding, to enhance OCR accuracy.
• Error Handling and Fallback Mechanism: Implemented a robust error handling system to apply fallback processing techniques if the initial OCR fails, ensuring higher success rates.
• User Interaction and File Handling: Streamlit’s widgets and components were used to allow users to upload multiple files, capture documents via camera, and download the processed data in various formats.

Skills Applied and Learned:

• Machine Learning and NLP: Gained hands-on experience with pre-trained models (LayoutLM) for text classification and segmentation tasks.
• Web Application Development: Strengthened skills in building interactive web applications using Streamlit, including handling file uploads, real-time camera integration, and managing user inputs.
• Data Processing and Manipulation: Enhanced knowledge in handling and processing different document formats (images, PDFs) and converting data between various formats (text, PDF, Excel).
• Error Handling and Resilience: Learned to implement effective error-handling strategies to ensure the robustness and reliability of the application.
• Image Processing: Applied image processing techniques to improve OCR results and learned how to draw annotated overlays on images to visualize detected text segments.

Project Goals:

1. Automate Text Extraction: Develop a tool that can efficiently extract text from various document types with minimal user intervention.
2. Enhance Accuracy with Machine Learning: Utilize advanced machine learning models to accurately classify and segment extracted text into meaningful categories.
3. Provide Multi-Format Downloads: Allow users to download the extracted text in different formats (Text, PDF, Excel) for ease of use and further analysis.
4. Ensure User Accessibility: Create a user-friendly interface that supports multiple file uploads, real-time document capture via cameras, and intuitive interaction for users of all technical levels.
5. Robust Error Handling: Implement fallback mechanisms to ensure the tool remains effective even when initial processes fail, increasing overall reliability.

This project demonstrates a comprehensive integration of machine learning, OCR, and web development technologies to create a practical solution for document processing tasks.

Project 5

Title : Building an Autonomous Self-Driving Car Road Detection/Segmentation

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Problem Statement

Objective:

The primary objective of this project is to develop a robust and efficient system for real-time road detection and segmentation in autonomous self-driving cars. The system leverages state-of-the-art machine learning and computer vision techniques to identify and segment various road elements, such as lanes, vehicles, and obstacles, to facilitate safe and accurate navigation.

Background:

With the rapid advancement of autonomous vehicle technology, accurate road detection and segmentation have become critical for ensuring the safety and reliability of self-driving cars. These vehicles must be able to interpret their surroundings accurately, identify lanes, detect obstacles, and understand road conditions in real-time to make informed decisions. This project addresses these challenges by integrating object detection and semantic segmentation techniques to create a comprehensive road perception system.

Problem Statement:

The challenge is to design and implement a system capable of processing video input from a vehicle’s camera to perform the following tasks in real-time:

• Road Detection: Identify road lanes and segment different parts of the road (e.g., lanes, sidewalks, and other structures).
• Object Detection: Detect and classify various objects on the road, such as vehicles, pedestrians, and traffic signs.
• Road Segmentation: Overlay the detected objects and road elements on the original video feed, providing a clear visual representation for decision-making in autonomous driving.
• Speed Optimization: Ensure that the system operates at high speed, drastically increasing video playback speed while maintaining accuracy to handle real-world driving scenarios efficiently.

Software and Tools Used:

1. Python: The primary programming language used for developing the project.
2. OpenCV: Used for video processing, object detection, and image manipulation tasks.
3. TensorFlow: Utilized for implementing the semantic segmentation model (DeepLabv3) and other machine learning tasks.
4. Mediapipe: Employed for face detection during the login process, ensuring secure access.
5. Streamlit: Used to create a user-friendly web interface for video uploading, processing, and displaying the results.
6. FFmpeg (Optional): For converting GIFs to MP4 for header display.
7. NumPy: For efficient numerical computations and handling array-based data structures.

Techniques Applied:

1. Object Detection (YOLOv4-tiny): A real-time object detection algorithm used to detect and classify objects such as cars, pedestrians, and traffic signs.
2. Semantic Segmentation (DeepLabv3): A deep learning technique that segments various elements of the road, such as lanes and sidewalks, by assigning a label to every pixel in the input image.
3. Image Processing: Techniques such as edge detection, region of interest (ROI) selection, and overlay creation for visual enhancements in the segmented video output.
4. Frame Rate Optimization: Techniques to increase video playback speed, ensuring real-time processing suitable for autonomous driving scenarios.

Skills Applied and Learned:

1. Computer Vision: Enhanced understanding of video processing, object detection, and image segmentation using OpenCV and deep learning models.
2. Deep Learning: Gained experience in deploying and fine-tuning pre-trained models such as DeepLabv3 for semantic segmentation.
3. Real-time Processing: Developed skills in optimizing code for real-time video processing, crucial for applications like self-driving cars.
4. Web Development with Streamlit: Learned how to create interactive web applications that allow users to upload, process, and visualize video data.
5. User Authentication: Applied techniques in user authentication using face detection to ensure secure access to the application.
6. Project Management: Experience in integrating multiple software tools and techniques to solve a complex problem in a cohesive manner.

Conclusion:

This project demonstrates the integration of advanced computer vision and deep learning techniques to address the critical problem of road detection and segmentation for autonomous vehicles. The system developed is not only capable of processing video feeds in real-time but also enhances the understanding of road environments, which is crucial for the safe operation of self-driving cars. Through this project, significant skills in software development, machine learning, and real-time video processing were applied and further refined.

Project 6

Title : Object Detection and Tracking App with Image, Video, Live Camera, and Google Search Integration

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Problem Statement:

Project Title: Object Detection and Tracking App with Image, Video, Live Camera, and Google Search Integration

Objective: The goal of this project is to develop a comprehensive web application capable of performing real-time object detection and tracking across different media inputs, including images, videos, and live camera feeds. The application also integrates with Google Search to provide additional information and high-definition images related to the detected objects.

Challenges:

1. Real-time Object Detection: The project requires the implementation of an efficient object detection model that can process images, videos, and live camera feeds in real time.
2. Integration with External APIs: The application needs to integrate with Google Custom Search API to fetch relevant images and information based on the detected objects.
3. User Interface: The application must provide a user-friendly interface for uploading images and videos, selecting object detection parameters, and viewing results.
4. Performance Optimization: The system needs to handle different media types without significant delays, ensuring smooth and accurate object detection and display.

Technologies and Tools Used:

Programming Languages:

• Python: Core programming language used for the entire application.

Libraries and Frameworks:

• Streamlit: Used to create the web application interface, allowing for easy deployment and interaction.
• OpenCV: Employed for image and video processing, including object detection and bounding box creation.
• YOLO (You Only Look Once): Utilized for real-time object detection. The pre-trained YOLO model is loaded for transfer learning to detect and classify objects.
• Pandas: Used for handling and displaying detected object data in tabular form.
• Selenium and BeautifulSoup: Used for web scraping and integrating Google Search functionality into the application.
• NumPy: For numerical operations and manipulation of image data.
• PIL (Python Imaging Library): Used for handling and processing images.

APIs:

• Google Custom Search API: Integrated to allow users to search for high-definition images and related information for detected objects.

Development Environment:

• Streamlit for building and deploying the application.
• Tempfile and OS modules for handling temporary files during video processing.

Skills Applied and Learned:

1. Machine Learning and Computer Vision: Implementing YOLO for object detection and understanding the intricacies of working with pre-trained models and transfer learning.
2. Web Development: Building an interactive web application using Streamlit, integrating various media input types, and ensuring a smooth user experience.
3. API Integration: Learning how to interact with external APIs like Google Custom Search to fetch additional data and images.
4. Real-time Processing: Gaining experience in handling and processing real-time video feeds and live camera input for object detection.
5. Data Manipulation: Using Pandas to manage and display detected object data effectively.

Expected Outcome:

The completed application will allow users to upload images, videos, or use a live camera feed to detect objects in real time. The application will also provide additional information and high-definition images of detected objects through Google Search, making it a powerful tool for object recognition and exploration.

Project 7

Title : Building a Pose Detection APP : Real life Object Tracking

Demo

source : https://youtu.be/YqPqPzzzpnc

Project files and source code

Problem Statement:

Project Overview: The objective of this project is to develop a web-based application that allows users to detect and analyze human poses using images, videos, or live camera feeds. This application aims to provide real-time feedback on posture, movement, and pose alignment, which can be utilized in various fields such as fitness, physical therapy, sports analysis, and ergonomics.

Problem Statement: In modern health, fitness, and sports environments, accurate pose detection and analysis are critical for improving performance, preventing injuries, and ensuring proper technique. However, existing solutions are often expensive, require specialized hardware, or are not user-friendly. This project seeks to address these challenges by developing an accessible, web-based solution that leverages advanced pose detection algorithms to provide real-time pose analysis using only a camera and a web browser.

Software and Tools Used:

• Streamlit: A Python-based framework used to build the interactive web application.
• OpenCV: An open-source computer vision library used for image and video processing.
• MediaPipe: A machine learning framework by Google that provides pre-trained models for pose detection.
• NumPy: A fundamental package for scientific computing with Python, used for numerical operations.
• Streamlit WebRTC: A plugin for Streamlit that enables real-time video streaming and processing through WebRTC technology.

Techniques Applied:

• Pose Detection: Utilizing MediaPipe’s pose detection model to identify and track key human landmarks (e.g., shoulders, elbows, hips) in images, videos, and live streams.
• Image Processing: Using OpenCV to process images and videos, converting them to suitable formats, resizing, and overlaying detected landmarks.
• Real-time Video Processing: Implementing WebRTC for live video streaming, enabling real-time detection and analysis of human poses directly from the user’s camera.
• Data Visualization: Drawing and labeling landmarks on the human body to provide clear visual feedback to the user regarding their posture and movement.
• Angle Calculation: Calculating angles between key body joints to assess alignment and detect deviations from ideal postures.

Skills Applied and Learned:

• Python Programming: Writing efficient and modular code in Python to integrate various libraries and tools.
• Machine Learning Integration: Applying pre-trained models for pose detection and understanding how to adapt them for real-time applications.
• Web Development: Using Streamlit to create an interactive, user-friendly web interface that allows users to upload images, videos, or access live camera feeds.
• Computer Vision: Gaining experience with OpenCV for image processing tasks, such as color conversion, drawing on images, and frame resizing.
• Real-time Data Processing: Learning to handle real-time data streams using WebRTC, including managing video feed inputs and outputs dynamically.
• User Interface Design: Developing a customizable user interface with Streamlit, enabling users to adjust various parameters (e.g., text size, colors) for a personalized experience.

Conclusion: This project provides a comprehensive solution for pose detection and analysis, blending modern machine learning techniques with real-time video processing to create an accessible and effective tool for various applications. The skills and technologies applied in this project highlight the integration of computer vision, machine learning, and web development, resulting in a product that is both functional and easy to use for end-users.

Project 8

Title : Product Price Prediction | Image Scraping | Analysis & Deployment App

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Problem Statement:

Objective:

The primary objective of this project is to develop a web application that allows users to predict the prices of various mobile phone models based on selected specifications and display corresponding images of the phones. The application also provides comparative visualizations of the predicted prices across different brands and models, facilitating an in-depth analysis for potential buyers, sellers, or market analysts.

Problem Description:

In the current market, mobile phones are available in a wide range of models, each with unique features and pricing. Consumers often face challenges in determining the fair price of a mobile phone, especially when comparing different brands or models with similar specifications. This project aims to address this challenge by developing a predictive model that estimates the price of a mobile phone based on its features, such as brand, model, release year, screen size, battery capacity, RAM, storage, camera resolution, and processor speed. Additionally, the project integrates image scraping techniques to fetch and display high-resolution images of the selected phone models.

Software Used:

1. Python: The primary programming language used for developing the application.
2. Streamlit: A Python framework used to build and deploy the interactive web application.
3. Selenium: A web scraping tool used to automate the process of fetching images from Google.
4. Pandas: Used for data manipulation and creating dataframes for feature selection and predictions.
5. Plotly: A graphing library used for creating interactive visualizations, such as bar charts and sunburst charts.
6. scikit-learn: Utilized for building and deploying the machine learning model for price prediction.
7. PIL (Python Imaging Library): Used to handle and resize images.

Techniques Used:

1. Web Scraping with Selenium: Automated the process of scraping high-resolution images of mobile phones from Google Images.
2. Machine Learning: A predictive model was built using scikit-learn to estimate the price of a mobile phone based on its specifications.
3. Data Manipulation: Pandas was extensively used for handling and manipulating data related to mobile phone specifications and prediction results.
4. Interactive Data Visualization: Plotly was used to create dynamic visualizations, including bar charts for comparative analysis and sunburst charts to represent the hierarchical relationship between brands and models.
5. Image Handling: PIL was used to download, process, and display images in the Streamlit app.

Skills Applied and Learned:

1. Python Programming: Applied and improved Python coding skills, particularly in data science, web scraping, and image processing.
2. Web Scraping with Selenium: Gained hands-on experience with Selenium for automating web browsing tasks and scraping data.
3. Data Manipulation with Pandas: Developed skills in manipulating and analyzing data using Pandas, including handling missing values and preparing data for machine learning models.
4. Machine Learning: Applied machine learning techniques to build a regression model for price prediction and understood the workflow of training, saving, and deploying a model.
5. Web Application Development with Streamlit: Learned to build and deploy interactive web applications using Streamlit, focusing on user experience and dynamic content rendering.
6. Data Visualization with Plotly: Developed proficiency in creating interactive visualizations with Plotly, allowing for better data interpretation and presentation.
7. Image Processing with PIL: Improved skills in image processing, including downloading, resizing, and displaying images within a web application.
8. Problem-Solving: Addressed challenges related to data consistency, model accuracy, and user interface design, leading to the successful completion of the project.

Conclusion:

This project demonstrates the integration of various data science and software engineering techniques to solve a real-world problem related to mobile phone price prediction. By combining machine learning, web scraping, and interactive visualizations, the developed application provides a comprehensive tool for users to analyze and compare mobile phone prices effectively. The project also highlights the importance of combining technical skills in programming, data analysis, and web development to create a functional and user-friendly application.

Project 9

Title : Building a Customer Segmentation App

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Overview:

The goal of this project is to analyze customer behavior using a real-world retail dataset and apply machine learning techniques to segment customers based on their purchasing habits. The analysis will help in identifying distinct customer segments, which can be leveraged for targeted marketing strategies, personalized customer experiences, and improved business decision-making.

Problem Statement:

Retail businesses often face challenges in understanding the diverse behavior of their customers. With thousands of transactions occurring daily, it becomes essential to identify patterns and segment customers based on their purchasing habits. However, manually analyzing such large volumes of data can be time-consuming and prone to errors. The objective of this project is to automate the customer segmentation process using machine learning techniques, thereby enabling businesses to gain actionable insights and enhance their marketing strategies.

Objectives:

1. Data Collection and Preprocessing:

• Collect and preprocess the customer transaction data from a publicly available dataset.
• Handle missing data, outliers, and perform feature engineering to create meaningful variables for analysis.

1. Customer Segmentation Using KMeans Clustering:

• Apply the KMeans clustering algorithm to segment customers based on their purchasing behavior.
• Identify and analyze distinct customer segments to understand their characteristics and preferences.

1. Visualizing Clusters and Customer Segments:

• Visualize the clustering results using scatter plots and other relevant plots to interpret the customer segments.
• Group product descriptions by country and visualize the top products in each country using a sunburst plot.

1. Building a Streamlit Application:

• Develop an interactive Streamlit application that allows users to upload datasets, perform customer segmentation, and visualize the results.
• Provide an interface for users to explore the data, view segmentation results, and analyze the top products in each segment.

Software and Tools Used:

Python:

• The primary programming language used for data analysis, machine learning, and web application development.

Pandas:

• Used for data manipulation and preprocessing.

Scikit-learn:

• Applied for implementing the KMeans clustering algorithm and preprocessing techniques.

Plotly:

• Used for creating interactive visualizations, including scatter plots and sunburst plots.

Streamlit:

• Developed the interactive web application to allow users to upload datasets, run clustering algorithms, and visualize results.

OpenPyXL:

• Used for handling Excel files during the data upload process.

Techniques and Methods Applied:

1. Data Preprocessing:

• Handling missing values, removing outliers, and creating new features to enhance the analysis.

Feature Engineering:

• Creating new features such as ‘TotalPrice’ to summarize customer transactions.

PCA (Principal Component Analysis):

• Applied for dimensionality reduction before clustering to improve computational efficiency and visualization.

KMeans Clustering:

• Segmented customers into distinct groups based on their purchasing behavior using the KMeans algorithm.

Data Visualization:

• Created scatter plots to visualize customer segments.
• Generated sunburst plots to represent the top products in each country and segment.

Web Development:

• Built an interactive Streamlit application for easy data exploration, clustering, and visualization.

Skills Applied and Learned:

Data Analysis:

• Enhanced the ability to analyze and preprocess large datasets.
• Developed feature engineering techniques to extract meaningful insights from raw data.

Machine Learning:

• Gained experience in applying unsupervised learning techniques, particularly KMeans clustering, to real-world data.

Data Visualization:

• Improved skills in creating interactive visualizations using Plotly to effectively communicate insights.

Web Development:

• Learned to build and deploy a user-friendly web application using Streamlit.

Problem-Solving:

• Applied analytical thinking to solve complex data problems, such as customer segmentation and product grouping.

Project Management:

• Developed a systematic approach to managing and executing data-driven projects from start to finish.

Conclusion:

This project successfully demonstrated how machine learning can be used to gain actionable insights from customer transaction data. The use of KMeans clustering allowed for the identification of distinct customer segments, which can be further analyzed to develop targeted marketing strategies. The interactive Streamlit application provides a user-friendly interface for businesses to explore their data, perform customer segmentation, and visualize the results effectively. The skills learned and applied during this project contribute to a comprehensive understanding of customer behavior analysis and the use of machine learning techniques in retail analytics.

Project 10

Title: Building a Predictive Analytics App for Crime Data Analysis with Live Deployment

Demo

source : my youtube channel : https://www.youtube.com/@NancyTicharwa

Project files and source code

Problem Statement

Overview:

This project focuses on developing a comprehensive predictive analytics application that analyzes crime data to help law enforcement agencies and policymakers make data-driven decisions. The application allows users to upload crime data, perform exploratory data analysis, filter data based on various criteria, and build predictive models to forecast crime occurrences. The application also includes visualizations for comparative analysis over different periods, enabling users to understand trends and patterns in crime data.

Objective:

The main objective of this project is to create an interactive and user-friendly web application that can:

1. Provide insights into crime data through various summary statistics and visualizations.
2. Allow advanced filtering and analysis of data based on user-selected criteria such as crime type, location, and time.
3. Build and evaluate predictive models using machine learning techniques.
4. Enable users to make predictions based on selected features and visualize the results.

Software Used:

• Streamlit: For creating the interactive web application.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Scikit-learn: For machine learning model building, including data preprocessing, model training, and evaluation.
• Matplotlib: For creating static visualizations.
• Plotly: For creating interactive plots and visualizations.
• Folium: For creating interactive geographical maps.
• Pydeck: For rendering high-performance, large-scale geographical visualizations.
• Joblib: For saving and loading machine learning models.
• GeoPandas: For handling geographical data.

Techniques Used:

• Data Preprocessing: Cleaning and transforming raw crime data, handling missing values, and encoding categorical variables.
• Exploratory Data Analysis (EDA): Generating summary statistics, identifying trends, and visualizing data distributions.
• Feature Engineering: Creating new features such as crime rate over time, and extracting temporal information from date columns.
• Machine Learning:
• Model Building: Using Random Forest Classifier to predict crime occurrences (e.g., whether an arrest will be made).
• Hyperparameter Tuning: Optimizing model performance by adjusting parameters like the number of trees, maximum depth, and minimum samples required for splitting.
• Cross-Validation: Evaluating the model’s generalization ability by performing cross-validation.
• Model Evaluation: Assessing model performance using metrics like classification reports and confusion matrices.
• Visualization:
• Geospatial Visualization: Using Folium and Pydeck for mapping crime data.
• Comparative Analysis: Visualizing and comparing crime data across different time periods.
• Feature Importance: Displaying the importance of features in the predictive model.

Skills Applied or Learned:

• Web Application Development: Building interactive applications using Streamlit.
• Data Wrangling and Cleaning: Handling real-world data, managing missing values, and performing data transformations.
• Machine Learning: Understanding model training, evaluation, and hyperparameter tuning.
• Data Visualization: Creating intuitive and interactive visualizations using Plotly and other libraries.
• Geospatial Analysis: Mapping crime data and understanding spatial relationships using Folium and Pydeck.
• User Authentication: Implementing basic user authentication for secure access to the application.

This project integrates various data science skills, including data preprocessing, machine learning, and visualization, to build a robust and interactive application. The application empowers users to explore crime data, gain insights, and make predictions, all within a user-friendly interface. The skills and techniques applied in this project are crucial for developing real-world data-driven applications that provide actionable insights and support decision-making processes.

SUMMARY

In summary, building a hands-on real life projects are what is going to get you your dream job. If you are looking to make a career in Data Science and A.I, I making spending time to practice and building your portfolio your №1 priority.

Other articles:

1. Best Ways to Search for Data Science Jobs 🚀
2. The BEST & PROVEN way to Land a Job as a Data Scientist
3. How Hollywood is Using Generative AI

Follow me for more at:

• Linkedin : https://www.linkedin.com/in/nancy-ticharwa/
• Medium : https://medium.com/@nancyticharwa1