This article provides a list of ML project ideas with relevant dataset links that you can use to build your portfolio and enhance your resume. Let's begin!
1. Food delivery Time prediction ML Model [Dataset link]
We've all ordered food and saw the long time it takes for the restaurant to cook and food and the delivery partner to deliver the food to us. In this project, you'll be building a Food delivery time predictor machine learning model.
Use Case:
Because it helps to control expectations and enhance the overall customer experience, food delivery time prediction is a useful tool for both customers and businesses. In this project, machine learning algorithms will be used to forecast an order's delivery time depending on a number of variables, including the distance between the restaurant and the delivery location, the type of cuisine, and the time of day.
How to start:
Gathering information about previous orders, including the delivery time and pertinent details, is the initial step in this endeavor. After pre-processing, the data will be divided into training and testing sets. The chosen machine learning model will subsequently be trained using the training set of data. The testing data can be used to assess the model's correctness.
Accomplishments:
This project is a fantastic chance for you to show off your abilities in feature engineering, data analysis, and model selection. Additionally, it might help you better grasp how machine learning algorithms can be used to solve actual issues and raise client happiness in the food delivery sector.
2. Weather forecasting Project [Dataset link]
Thats right, build a weather forcasting project!Use Case:
Weather forecasting is a crucial aspect of everyday life, as it affects various industries such as agriculture, transportation, and energy. This project involves using machine learning algorithms to predict weather conditions such as temperature, humidity, and precipitation.How to start:
The first step in this project is to gather historical weather data, including the target variables and relevant features such as atmospheric pressure, wind speed, and cloud coverage. The data will then be pre-processed and split into training and testing sets. A machine learning model will be selected and trained on the training data. The accuracy of the model will be evaluated using the testing data.Accomplishments:
This project is a great way to showcase your skills in data analysis, feature engineering, and model selection. Additionally, it provides a hands-on experience in applying machine learning algorithms to solve real-world problems and improve the accuracy of weather forecasts.3. Clustering Music Genres Project [Dataset link]
Clustering music genres is a project that involves using machine learning algorithms to categorize songs into different genres based on their musical attributes. This project can be useful for music streaming platforms, music recommendations systems, and musicologists.
How to begin:
The first step in this project is to gather a large dataset of songs, along with their musical attributes such as tempo, rhythm, melody, and harmony. The data will then be pre-processed and transformed into a suitable format for machine learning algorithms. Next, a clustering algorithm such as K-Means will be applied to group similar songs into genres. The results can be evaluated based on metrics such as accuracy and stability.Accomplishments:
This project provides an opportunity to showcase your skills in data analysis, feature engineering, and unsupervised learning. Additionally, it provides hands-on experience in applying machine learning algorithms to a real-world problem in the music industry and can lead to new insights into the relationship between musical attributes and genre.4. Credit score classification Project [Dataset link]
Credit scoring is a crucial aspect of the financial industry, as it helps to evaluate the creditworthiness of individuals and determine their ability to repay loans. This project involves using machine learning algorithms to classify individuals into different credit score ranges based on their financial history and other relevant factors.How to begin:
The first step in this project is to gather a dataset of individuals with their credit history, financial information, and other relevant factors. The data will then be pre-processed and split into training and testing sets. A suitable machine learning model such as a decision tree or logistic regression will be selected and trained on the training data. The accuracy of the model will be evaluated using the testing data.Accomplishments:
This project is a great opportunity to demonstrate your skills in data analysis, feature engineering, and supervised learning. Additionally, it provides hands-on experience in applying machine learning algorithms to a real-world problem in the financial industry and can lead to improved credit scoring systems that are more accurate and fairer.Hopefully this list will help you shine on your resume, so go pick up your tools and languages, and start building your next big thing. Keep reading and stick around! :)