. Use the Titanic dataset to build a model that predicts whether a passenger on the Titanic survived or not. This is a classic beginner project with readily available data. . The dataset typically used for this project contains information about individual passengers, such as their age, gender, ticket class, fare, cabin, and whether or not they survived. Dataset - https://www.kaggle.com/datasets/brendan45774/test-file
. Build a model that predicts the rating of a movie based on features like genre, director, and actors. You can use regression techniques to tackle this problem. . The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics. . Movie Rating Prediction project enables you to explore data analysis, preprocessing, feature engineering, and machine learning modeling techniques. It provides insights into the factors that influence movie ratings and allows you to build a model that can estimate the ratings of movies accurately. Dataset - https://www.kaggle.com/code/sherinclaudia/movie-rating-prediction/notebook
. The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements. Now, imagine that you have the measurements of Iris flowers categorized by their respective species. Your objective is to train a machine learning model that can learn from these measurements and accurately classify the Iris flowers into their respective species. . Use the Iris dataset to develop a model that can classify iris flowers into different species based on their sepal and petal measurements. This dataset is widely used for introductory classification tasks. Dataset - https://www.kaggle.com/datasets/arshid/iris-flower-dataset
. Sales prediction involves forecasting the amount of a product that customers will purchase, taking into account various factors such as advertising expenditure, target audience segmentation, and advertising platform selection. . In businesses that offer products or services, the role of a Data Scientist is crucial for predicting future sales. They utilize machine learning techniques in Python to analyze and interpret data, allowing them to make informed decisions regarding advertising costs. By leveraging these predictions, businesses can optimize their advertising strategies and maximize sales potential. Let's embark on the journey of sales prediction using machine learning in Python. Dataset - https://www.kaggle.com/code/ashydv/sales-prediction-simple-linear-regression/input
. Build a machine learning model to identify fraudulent credit card transactions. . Preprocess and normalize the transaction data, handle class imbalance issues, and split the dataset into training and testing sets. . Train a classification algorithm, such as logistic regression or random forests, to classify transactions as fraudulent or genuine. . Evaluate the model's performance using metrics like precision, recall, and F1-score, and consider techniques like oversampling or undersampling for improving results. Dataset - https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud