Top 10 Python Machine Learning Projects
Machine Learning is the idea that allows the machine to learn from the examples and experience without being explicitly programmed. Machine learning is about creating and implementing an algorithm that let machine receive data and use this data to:
- Make predictions
- Analyze patterns
- Give recommendations on its own
ML cannot be implemented without data.
Machine Learning has a variety of applications. You will not get the practical insight into this technology unless and until you work on real-time projects.
In this article, you will 10 machine learning projects for getting started into the machine learning world. These projects will help you to gain real-world experience with this growing technology.
1. Iris Classification
The Iris Flower Dataset is the machine learning project which is one of the best datasets for classification. The goal of this project is to classify the flowers into among the three species – virginica, setosa, or versicolor based on length and width of petals and sepals. This project is often referred to as the “Hello World” of machine learning. The dataset is small and easy to handle.
2. House Price Prediction
In this project, we need to create a regression model that can accurately predict the price of the house depending on various features. The price of the house depends on various factors such as location, no. of rooms, crime rate, etc. By taking advantage of all of the feature variables available to use and use it to analyze and predict house prices.
3. Wine Quality Prediction
In this project, we need to build a model to predict the quality of the wine. The task is to predict the quality of wine on a scale of 0-10. You can solve this as a regression problem. You have to use the chemical information of the wine and build the machine learning model which will give the result of wine quality.
4. Customer Segmentation
Customer segmentation is the process of dividing the customer on the basis of their purchases, age, gender, interest, etc. The objective behind doing so is to identify potential customers, personalize marketing. In this project, you have to apply unsupervised machine learning to identify different groups or clusters.
5. Customer Churn Prediction
Customer Churn refers to the process of identifying all the possible the customer or clients who will terminate their relations with the company. It is a very important factor for any organization as it used to estimate the growth of the organization but also for predicting trends of future customers. In this project, you have to do classification to identify the customers.
6. Recommendation System
Recommendation System is the algorithm that helps the user discover products or content by predicting the user’s rating of each item and then showing them the items, they would rate highly. It is everywhere now, be it an online purchasing app, movie streaming app, or music streaming. About Amazons, 30% of revenue comes from recommendations of product. Building any recommendation system can surely enhance your skills as well as profile.
7. Sentiment Analysis
Sentiment Analysis is a procedure used to determine if a piece of writing is positive, negative, or neutral. It is a type of data mining that measures people’s opinions through Natural Language Processing. It is a good project to learn how does Natural Language Processing Work and use it.
8. Credit Card Fraud Detection
In this project, you have to recognize the fraudulent credit card transactions. You have to Classify the transactions into valid and fraud cases. It is a great problem to learn about classification.
9. Stock Price Prediction
Stock Price prediction is the technique of predicting the future price of the company stock or other financial instruments traded on a financial exchange. It is a very good project to learn about time series analysis. You have to use algorithms like Auto Arima, LSTM to build the model and predict the accurate stock price.
10. Human Activity Recognition
Human activity recognition is the project of predicting the movement of a person such as running, cycling using sensor data in smartphones. In this, you will need the fitness activity records. You have to build a classification model to predict future activities.
To do practice you can find many datasets on websites like Kaggle, UCI machine learning repository. Hope this article will help you to get the basic idea of the implementation of Machine Learning in our day to day life.