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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 5 Free Data Science Projects With Solutions

Nitika Sharma
Nitika Sharma Last Updated : 10 Dec, 2024
5 min read

Introduction

Are you eager to dive into data science and sharpen your skills? Look no further! This article will explore five exciting data science projects with step-by-step solutions. Whether you’re a novice looking to learn or an experienced data enthusiast seeking to expand your portfolio, these hands-on free data science projects will empower you to conquer real-world challenges. Best of all, they won’t cost you a dime. Let’s embark on this data-driven journey and discover how you can enhance your data science expertise, one project at a time!

In this article, you will discover effective data science solutions, explore free guided projects, and learn about online data science projects to enhance your skills.

Table of contents

Importance of Data Science Projects

For several compelling reasons, data science projects play a pivotal role in the field. Firstly, they provide a bridge between theoretical knowledge and practical application, allowing data scientists to test and implement what they’ve learned in real-world scenarios. These projects serve as invaluable learning experiences, refining data collection, cleaning, analysis, visualization, and modeling skills.

Moreover, completed data science projects serve as building blocks for a strong portfolio, enhancing job prospects and freelance opportunities. They also sharpen problem-solving abilities and critical thinking, as many projects involve tackling complex challenges. Additionally, data scientists often gain domain-specific knowledge depending on the project’s subject matter, making them more effective in specific industries.

Furthermore, data science projects offer insights that support informed decision-making, empowering businesses to optimize processes and identify growth opportunities. They encourage innovation by pushing the boundaries of data analysis techniques. Collaboration on projects fosters teamwork and communication skills, which are crucial in professional settings. Lastly, these projects promote continuous learning and adaptation to evolving tools and techniques, ensuring data scientists remain at the forefront of the field.

Also Read: Top 10 Data Science Projects with Source Code

Top 5 Free Data Science Projects

  • Loan Eligibility Classification
  • Sentiment Analysis and Text Classification
  • Web Scraping with Python
  • Sales Prediction with Regression
  • Time Series Forecasting

Project 1: Loan Eligibility Classification

This project focuses on binary classification, particularly for loan eligibility. You’ll work on a case study involving Dream Housing Finance, an organization dealing with home loans. Your task is to automate the loan eligibility process based on customer details provided during online applications.

Loan Eligibility Classification | Free Data Science Project

How to Solve?

You’ll learn various approaches to classification problems through this course. It provides hands-on experience in solving the loan eligibility classification problem using Python.

Tools Required

Python, libraries for machine learning and classification.

Solution Index

  • Problem Statement
  • Hypothesis Generation
  • Exercise 2 | Discussion
  • Getting the system ready and loading the data
  • Understanding the Data
  • Univariate Analysis
  • Bivariate Analysis
  • Missing Value and Outlier Treatment
  • Evaluation Metrics for Classification Problems
  • Model Building : Part I
  • Logistic Regression using stratified k-folds cross validation
  • Feature Engineering
  • Model Building : Part II

Here is the solution for this free data science project.

Project 2: Twitter Sentiment Analysis

This project delves into natural language processing (NLP) and text analysis. You’ll work on sentiment analysis, which is essential for understanding public opinions and comments on products or social media.

Sentiment Analysis using transformers

How to Solve?

The course equips you with the skills and techniques needed for text classification and sentiment analysis using Python. You’ll gain hands-on experience in solving such problems.

Tools Required

Python, NLP libraries, and sentiment analysis tools.

Solution Index

  • Loading Libraries and Data
  • Data Inspection
  • Data Cleaning
  • Story Generation and Visualization from Tweets
  • Bag-of-Words Features
  • TF-IDF Features
  • Word2Vec Features
  • Modeling
  • Logistic Regression
  • Support Vector Machine (SVM)
  • RandomForest
  • XGBoost
  • FineTuning XGBoost + Word2Vec

Here is the solution for this free data science project.

Project 3: Web Scraping with Python

Summary: Web scraping is crucial for gathering data from websites, especially when APIs aren’t available. This course introduces web scraping basics using Python and guides you through a real-world web scraping project.

Octoparse-Web-Scraping

How to Solve?

You’ll learn the fundamentals of web scraping, explore Python libraries for web scraping, and implement web scraping in a practical project.

Tools Required

Python, web scraping libraries.

Solution Index

  • Introduction to Web Scraping
  • Web Scraping: Procedure
  • Scraping URLs and Email IDs from a Web Page
  • Scrape Images in Python
  • Scrape Data on Page Load

Here is the solution for this free data science project.

Project 4: Sales Prediction with Regression

This project tackles the common real-life problem of sales prediction. You’ll work on the Big Mart Sales Prediction Challenge, learning regression techniques in R.

Big Mart Sales Prediction

How to Solve?

The course provides theory and practice materials to enhance your predictive modeling skills for solving regression problems.

Tools Required

R, regression analysis tools.

Solution Index

  • Problem Statement
  • Hypothesis Generation
  • Loading Packages and Data
  • Understanding the Data
  • Univariate Analysis
  • Bivariate Analysis
  • Missing Value Treatment
  • Feature Engineering
  • Encoding Categorical Variables
  • PreProcessing Data
  • Model Building
  • Linear Regression
  • Regularized Linear Regression
  • Random Forest
  • XGBoost

Here is the solution for this free data science project.

Project 5: Time Series Forecasting

This project delves into time series forecasting, a critical aspect of making informed business decisions. You’ll work with time-based data to derive insights for prediction and forecasting.

time series forecasting

How to Solve?

The course guides you through time series forecasting methods, helping you analyze data over time, make predictions, and plan ahead.

Tools Required

Time series analysis tools, statistical software.

Solution Index

  • Introduction to Time Series
  • Understanding Problem Statements and Data Sets
  • Exploration and Preprocessing
  • Modelling Techniques and Evaluation

Here is the solution for this free data science project.

Conclusion

In conclusion, free data science projects are the cornerstone of a data scientist’s journey. They offer a unique blend of practical application, skill enhancement, and portfolio development. These projects empower individuals to bridge the gap between theory and practice, honing their data manipulation, analysis, and modeling abilities.

As you embark on your data science project endeavors, remember that the learning process is ongoing. The skills and insights gained from these projects will continue to shape your career in this dynamic field.
If you want to take your data science expertise to the next level, consider enrolling in our BlackBelt Data Science Program. This advanced program will refine your skills, ensuring you can tackle complex data challenges.

Q1. How do I find data science project ideas? 

A. Generate project ideas by exploring real-world problems, seeking inspiration from online datasets, and participating in data science communities like Analytics Vidhya and GitHub.

Q2. What projects can I do with data science? 

A. Data science projects encompass a wide range, including predictive modeling, natural language processing, image analysis, recommendation systems, and more.

Q3. How do I get freelance data science projects?

A. To secure freelance data science projects, build a strong portfolio, network on platforms like Upwork and Freelancer, and showcase your skills and expertise on LinkedIn and personal websites.

Q4.What is the best project for data science?

Here are some popular data science project ideas:
Beginner: Customer segmentation, sentiment analysis, house price prediction
Intermediate: Fraud detection, recommendation systems, image recognition
Advanced: Natural language processing, time series forecasting, medical image analysis

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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