Top 10 Kaggle ML Projects to Become Data Scientists in 2024

Unlock your potential top 10 ML projects on Kaggle for future data scientists in 2024

The first steps toward becoming a Data Scientist in the field of Machine Learning are ML projects. Through practical experience working with real-world datasets, these projects help them hone their data science skills. Working on these projects helps data scientists gain a greater understanding of machine learning techniques and their applications, in addition to learning how to navigate complex datasets. Together, let's delve into the fascinating field of data science and develop your abilities to unprecedented levels in 2024.

Project 1: Dog Breed Classification

To categorize dog breeds from user-provided photographs, you will develop a deep learning model in this project by utilizing CNNs. You will work with the "Stanford Dogs Dataset" on Kaggle, which is a collection of categorized photos of different breeds. The task includes preprocessing the images, creating and training a CNN, and assessing the CNN's output using accuracy-related metrics. Implementation can make use of Python libraries like PyTorch or TensorFlow.

Project 2: Deploy Machine-Learning Model with Gradio

Gradio is an intuitive library that you will use in this project to deploy a machine-learning model. After choosing a dataset depending on the task, you will train a model taking accuracy and prediction latency into account, and then you will deploy the model. Model weights will be saved and integrated with Gradio as part of the project to enable interactive predictions. PyTorch, TensorFlow, and Gradio are among the technologies employed.

Project 3: Fake News Detection with NLP

You will develop a machine learning model in this project that uses natural language processing (NLP) to differentiate between authentic and fraudulent news articles. Using datasets such as the "Fake News Dataset" from Kaggle, you will preprocess text, extract features, and categorize. Algorithms such as Naive Bayes and NLTK are examples of technologies. Recall, F1 score, and precision will be used to assess the model's performance.

Project 4: Movie Recommendation System

To improve user experience on sites like Netflix, you will develop a recommendation system in this project that makes movie or series suggestions based on user history. You will use matrix factorization, collaborative filtering, and datasets such as MovieLens or IMDb. You will also use frameworks such as Surprise or LightFM. Mean Absolute Error will be used to assess the system's performance.

Project 5: Customer Segmentation

To provide individualized suggestions, you will develop a machine-learning model in this project that will segment clients based on their purchasing patterns. You will apply clustering methods like K-means using unsupervised learning and datasets from sites like Amazon or Flipkart. Data processing, visualization, cluster analysis, and assessment utilizing metrics such as the Silhouette score are all part of the project.

Project 6: Stock Price Prediction

You will use historical data and machine learning to forecast stock values in this project. On data such as Open, High, Low, Close, and Volume, you will do time series analysis and forecasting. LSTM networks, ARIMA, and autocorrelation are some of the methods. You will train a forecasting model and assess it using metrics such as Mean Squared Error after processing and breaking down the data.

Project 7: Speech Emotion Recognition

You will use machine learning to create a model in this project that can identify emotions in spoken languages. You will process, extract characteristics, and categorize emotions from audio data from the "RAVDESS" dataset. Among the methods are deep learning and signal processing. Accuracy and confusion matrix will be used to assess the model's performance.

Project 8: Sales Forecasting System

Using past data, you will develop a system in this project to forecast future sales. Inventory control and demand forecasting are critical functions for firms. To assess performance, you will use measures like Mean Squared Error or R-squared, preprocess sales data, and time series forecasting or regression models.

Project 9: Digit Classification System with MNIST Dataset

The MNIST dataset, a well-known introduction to image classification, will be used in this project to help you develop a model for classifying handwritten digits. Once the model is trained, the accuracy and confusion matrix will be used to assess performance. You will also preprocess the images and use TensorFlow or PyTorch to create a CNN architecture.

Project 10: Credit Card Fraud Detection

To identify fraudulent credit card transactions, you will develop a machine-learning model in this project. Using a labeled dataset of transactions, you will train the model, adjust parameters, examine performance using precision, recall, and ROC-AUC, use methods for anomaly detection and classification such as Random Forest or SVM, preprocess data, and refine the model.