Consider the following scenario: you want to buy or sell a house, or you are relocating to a new city and looking for a rented house.
In this project, you will manipulate the dataset in order to create a house price prediction model using XGBoost. Average income, number of hospitals, number of schools, crime rate, and other factors are considered.
Note: If you are a student and struggling with Machine Learning Project, you can get the best ideas from our experts.
2. Sales Prediction Project
As a beginner, you should work on a variety of machine learning project ideas to broaden your skill set. This dataset contains 2013 sales data for 1559 products from 10 different locations in various cities. The goal is to create a regression model that predicts the sales of all 1559 products in all 10 BigMart locations for the following year.
3. Music Recommendation System Project
Spotify will recommend similar songs based on the songs you've liked. How does the system accomplish this? This is an excellent example of how ML can be used. The first task is to predict the outcomes of a user listening to a song on loop within a given time frame. If the user has heard the same song within a month, the prediction is regarded as 1. The dataset contains a list of songs that were heard by which consumer and when.
4. Iris Flowers Classification ML Project
One of the most basic machine learning datasets in classification literature is Iris Flowers. This machine learning problem is commonly referred to as the "Hello World" problem in machine learning. The dataset contains numerical characteristics, and Machine Learning beginners must understand how to handle and load data.
5. Stock Prices Predictor with the help of TimeSeries
This is yet another intriguing machine learning project idea in the finance sector. A stock price predictor analyses a company's performance and forecasts future stock prices.
A time series is a chronological interpretation of event occurrences. A time series is investigated to identify patterns so that future incidents can be predicted based on trends observed over time. ARIMA (autoregressive integrated moving average), moving average, and exponential smoothing are some models that can be used for time series forecasting.
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