225 Machine Learning Projects with Python

 

Machine Learning Projects for Beginners

Before moving to the complex projects in the next section, I advise you to explore these beginner-level projects if you are new to Machine Learning. You only need knowledge of Python libraries like Numpy, Pandas, Malpotlib, Seaborn and Scikit-Learn to understand and work on the projects below:

1. WhatsApp Chats Analysis
2. Covid-19 Vaccine Analysis
3. Student Grades Prediction Model
4. Uber Trips Analysis
5. Google Search Analysis
6. Tesla Stock Price Prediction Model
7. Financial Budget Analysis
8. Click-Through Rate Prediction Model
9. Interactive Language Translator
10. Language Detection
11. Create a Chatbot with Python
12. Best Streaming Service Analysis
13. Stock Price Prediction
14. Data Science Project on President Heights
15. Data Science Project on Birth Rate Analysis
16. Data Science Project on Time Series
17. Data Science Project on Area and Population
18. A Complete Machine Learning Project Walkthrough
19. Text Summarization
20. Keyword Extraction

Advanced Machine Learning Projects

Now, these are the projects where you will deal with real-time problems. You need to have some knowledge of Python libraries like Scikit-Learn, TensorFlow, Keras, and Pytorch to understand and work on the projects below:

1. End-to-end Machine Learning Project
2. Profit Prediction Model
3. Automatic Time Series Forecasting
4. Ted-Talks Recommendation System
5. Real-time Sentiment Analysis
6. Amazon Recommendation System
7. Mobile Price Classification
8. Spotify Recommendation System
9. Text Emotions Detection
10. Hotel Recommendation System
11. Bankruptcy Prediction Model
12. Customer Personality Analysis
13. Instagram Algorithm
14. Diamond Price Prediction Model
15. Book Recommendation System
16. Netflix Data Analysis
17. Satellite Imagery Analysis
18. Topic Modelling
19. Covid-19 Vaccine Sentiment Analysis
20. Human Activity Recognition Model
21. Cohort Analysis
22. End to end Machine Learning Project
23. Life Expectancy Analysis
24. Bitcoin Price Prediction
25. Human Resource Analysis
26. Energy Consumption Prediction
27. LightGBM Model
28. FastAI Model
29. House Price Prediction
30. Real-Time Face Mask Detection
31. Netflix Recommendation System
32. Named Entity Recognition
33. Number Plate Detection
34. IPL Analysis with Python
35. Gold Price Prediction
36. Object Detection
37. Highest-Paid Athletes Analysis
38. Text Generation
39. Spelling Correction with Python
40. Income Classification
41. Customer Churn Prediction
42. Language Translation Model
43. Resume Screening
44. Sign Language Classification
45. Online Shopping Intention Analysis
46. Network Graph Analysis
47. Keyword Extraction
48. Amazon Best Selling Books Analysis
49. Covid-19 Cases Prediction for Next 30 Days
50. YouTube Trending Videos Analysis
51. Gender Classification
52. Flower Recognition
53. Pneumonia Detection
54. Employee Attrition Prediction
55. Hand Gesture Recognition
56. Restaurant Recommendation System
57. Face mask Detection
58. Market Basket Analysis using Apriori Algorithm
59. Breast Cancer Detection
60. Earthquake Prediction Model
61. Outlier Detection
62. Heart Disease Prediction
63. Plastic Users Analysis
64. Landmark Detection
65. Chatbot with Machine Learning
66. Next Word Prediction Model
67. Age and Gender Detection with Python
68. Autocorrect Keyboard with Python and Machine Learning.
69. Machine Learning in 5 lines of code.
70. Deepfake Detection with Machine Learning.
71. Predict US Elections with Python.
72. Fake Currency Detection with Machine Learning.
73. Predict Tinder Matches with Machine Learning.
74. Image Segmentation
75. Title Generator with Python.
76. Build Google Search Algorithm with Python.
77. Face Landmarks Detection
78. Pencil Sketch with Python.
79. OpenAI Gym in Machine Learning
80. Machine Translation Model
81. Covid 19 Analysis.
82. Build the TikTok Algorithm with Machine Learning.
83. Analyze ILP Matches.
84. Barcode and QR code Reader with Python
85. Extract Text From PDF with Python.
86. Predict IPL Winner 2020.
87. Predict Car Prices.
88. Analyze Call Records.
89. Create an API with Python.
90. Send Custom Emails with Python.
91. Colour Recognition with Machine Learning.
92. Create a 3D Video Animation.
93. Graph Algorithms in Machine Learning.
94. Image Features Extraction.
95. Rainfall Prediction with Machine Learning.
96. Classify Nationalities with Machine Learning.
97. Fraud Detection with Machine Learning.
98. Topic Modeling with Python
99. Named Entity Recognition
100. WhatsApp Group Chat Analysis
101. Translate Languages Using Python
102. Network Security Analysis
103. Bar Chart Race with Python
104. Keyword Research with Python
105. Fashion Recommendation System
106. Face Detection with Python
107. Emotion Detection Model
108. Telegram Bot with Python
109. Handwriting Recognition
110. Visualize a Solar System with Python.
111. Hate Speech Detection Model
112. Build Instagram Filters with Python.
113. Contact Tracing with Machine Learning
114. Deploy a Chatbot with Python into a Web Application
115. Summarize Text with Machine Learning
116. Language Classification with Machine Learning.
117. OTP Verification GUI with Python
118. Create an Audiobook with Python
119. Titanic Survival Analysis
120. Image Recognition with PyTorch
121. Gender Classification Model
122. Daily Births Forecasting
123. Binary Search Algorithm
124. Galaxy Classification with Machine Learning
125. Time Series with LSTM Model
126. Image Classification with TensorFlow
127. Predict Weather with Machine Learning
128. Create a Package with Python
129. Computer Vision with Python
130. Diamonds Analysis with Python
131. Text Classification with TensorFlow
132. Image Segmentation with Python
133. Ridge and Lasso Regression
134. Predict Fuel Efficiency
135. ABC Analysis with Python
136. Lung Segmentation with Machine Learning
137. RFM Analysis with Python
138. Build Neural Network with Python Code
139. Genetic Algorithm with Python
140. Predict Migration with Machine Learning
141. Analyze Healthcare Data
142. NLP For Other Languages
143. Data Science Project on Text and Annotations
144. Data Science Project on — Handwritten Digits
145. Data Science Project on- Support Vector Machines
146. Data Science Project — Stock Price Prediction with Machine Learning
147. Data Science Project on — Classification of Text
148. Data Science Project on-Extracting HOG Features
149. Data Science Project on — Amazon Products Reviews Sentiment Analysis with Machine Learning
150. Data Science Project — Email spam Detection with Machine Learning
151. Data Science Project — Heart Disease Prediction with Machine Learning
152. Data Science Project — Movie Recommendation System
153. Data Science Project — Student Performance Analysis with Machine Learning
154. Data Science Project on FIFA Analysis with python
155. Data science project — Fake News Classification
156. Data Science Project — DNA Sequencing with Machine Learning
157. Data Science Project — Book Recommendation System with Machine Learning
158. Data Science Project — Bitcoin Price Prediction with Machine Learning
159. Machine Learning Project — Artificial Neural Networks
160. Algorithmic Trading Strategy with Machine Learning and Python
161. Movie Reviews Sentiment Analysis -Binary Classification with Machine Learning
162. Data Science Project — Supermarket Sales Analysis
163. Data Science Project — GDP Analysis
164. Data Science Project — Predict Customer Churn with Python and Machine Learning
165. Data Science Project — San Francisco Crime Analysis
166. Machine Learning Project — Human Activity Recognition using Smartphone Data
167. Credit Card Fraud Detection with Machine Learning
168. Weather Forecasting with Machine Learning
169. SMS Spam Detection with Machine Learning
170. Covid-19 Detection with Machine Learning and AI
171. Customer Segmentation with Machine Learning
172. Employee Turnover Prediction with Machine Learning
173. Predict Diabetes with Machine Learning
174. Image Classification with PyTorch
175. Time Series Forecasting with ARIMA Model
176. Natural Language Processing on WhatsApp Chats
177. Fake News Detection Model
178. Image Classification with Artificial Neural Networks
179. Binary Classification Model
180. Data Augmentation with Deep Learning
181. Next Word Prediction Model.
182. Image Segmentation
183. XGBoost Algorithm in Machine Learning
184. Face Landmarks Detection
185. Image Filtering with Machine Learning
186. Audio Feature Extraction
187. Machine Translation Model
188. Gender Classification Model
189. Create a 3D Video with Python and Machine Learning.
190. Named Entity Recognition
191. WhatsApp Group Chat Analysis
192. Translate Languages Using Python
193. Covid-19 Projects with Machine Learning
194. Deep Learning Projects with Machine Learning
195. Recommendation System Projects with Machine Learning
196. Sentiment Analysis Projects with Machine Learning
197. Classification Projects For Machine Learning
198. Regression Projects for Machine Learning
199. Chatbot Projects with Python
200. Machine Learning Projects on Future Prediction
201. Machine Learning Projects for Resume
202. Best Data Science Projects for Resume
203. Machine Learning Projects on Social Media Analysis

Summary

I hope you liked this article on 225 machine learning projects solved and explained by using the Python programming language. Feel free to ask your valuable questions in the comments section below.

Application is the best way to learn. There are a number of books, blogs, videos, etc. out there about Machine Learning and its applications. Being a serial consumer of such content can easily lead you to fall into the trap of thinking you’re moving closer to competence when in truth you’re not.

The secret to knowing whether you’ve understood the applied aspects of Machine Learning is simple. Implement it for yourself. If you cannot, it does not mean that you’re stupid, it simply means there are gaps in your knowledge therefore you must go back to learn.

True comprehension comes from implementing, failing, learning from the failure, and implementing again.

This is one of the many reasons experienced practitioners would advise beginners to get started on projects as soon as possible. Another justification for project work is exposure. Working on projects, as close as possible to problems being solved in the real world, will give beginners a good understanding of what it is like to work in a real-world environment.

Knowing what projects to get started with can be challenging so here are some ideas:

#1 Solving A Personal Problem

We all have problems in our lives. Facing our problems is usually a massive growth opportunity but it can be daunting due to our innate fear of failure. Being able to feel the fear and proceed is an extremely valuable skill for our own lives and we can make it fun by using our machine learning skills.

Being able to spot problems and convert them into Machine Learning problems is a skill within itself, hence why I personally favor this method overall. Solving a problem you’ve identified on your own reveals your breadth of competence since you would be engaged in a number of tasks you may not be required to do, depending on your role.

For instance, deploying and monitoring machine learning models in production may need to be part of your core competencies as an ML engineer, but building your own project would provide you with crucial insights regarding other areas within an ML models pipeline, such as Data Acquisition.

#2 Code Machine Learning Algorithms From Scratch

I remember the day my line manager asked me to talk about decision trees — not because it was useful, he was merely being curious about ML methods. To cut a long story short, I got stumped. I was talking a whole bunch of nothing and it bugged me because I was so sure I understood how a Decision tree worked after I read about it.

My greatest understanding of various Machine Learning algorithms came when I started my Algorithms From Scratch Series. The idea was to learn about each algorithm and code them up from scratch then compare my implementation to the implementation provided by Scikit-Learn to see how I performed.

kurtispykes/ml-from-scratch

Contribute to kurtispykes/ml-from-scratch development by creating an account on GitHub.

github.com

This phase developed my understanding of the mechanics behind various Machine Learning models and I learned how to translate mathematical formulas into code.

The only bit of advice I have for someone embarking on this method is to try and start as simple as possible then build up. For example, start by implementing Linear Regression then extend your Linear Regression model to a Logistic Regression model.

#3 Recommendation Engines With MovieLens

YouTube, Amazon, and Netflix are all great examples of where recommendation engines have been applied to generate value for end-users. It’s not uncommon for us to expect there to be some level of personalization when we visit certain sites. Consequently, recommender systems have become extremely popular, and learning about them may be of particular interest.

MovieLens is a known dataset meaning there are many implementations online that could help if you ever get stuck. The dataset consists of 62,000 movies by 162,000 users. I’ve done some work with this dataset in the past which you could use as a starting point.

kurtispykes/recommender_system

The purpose of this project is to employ a comprehensive analysis of the movielens dataset to gain interesting insights…

github.com

#4 Fake News Detection

Whenever I hear the words “Fake News”, I can’t help but think of Donald Trump. While I did not agree with many of his sentiments and ideologies, his hate towards fake news was somewhat justifiable.

GIF by @snl on GIPHY

With so many people being connected via the likes of social media, Fake News can spread like a wildfire, and it often does. Distinguishing fake news is more critical than ever hence why Facebook has already created its own fake news detector to filter such from people's news feed. Leveraging Machine Learning and Natural Language Processing, you can build your own fake news classifier to detect fake news.

#5 Boston Housing Price Prediction

The Boston House Price dataset is an extremely popular resource that has been used to benchmark algorithms. The data contains information gathered by the U.S Census service regarding housing within the Boston area. Initially, it was published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air’, J. Environ. Economics & Management, vol.5, 81–102, 1978.

Photo by todd kent on Unsplash

The price of a house depends on various factors (i.e. number of rooms, location, proximity to schools, etc). Using ML is a good way to uncover the underlying patterns and estimate the value of a property based on various features.

While working on this project, you may decide to collect some more data and extend the predictions to houses beyond Boston.

Wrap Up

I definitely believe the project ideas shared in this article are good for developing your intuition which is extremely necessary. However, when it comes to getting hired, I believe you should do slightly more if you wish to stand out. This doesn’t necessarily mean doing more projects. Instead, I would suggest focusing on doing 1 or 2 projects and doing them really well.

I’m a massive fan of Vin Vashista’s videos on YouTube. I’d definitely recommend you check out his video on Building Independent Data Science Projects That Get You Hired if you’re interested in taking your projects to the next level.

Thanks for Reading!

If you enjoyed this article, connect with me by subscribing to my FREE weekly newsletter. Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.