Chapter: Guided Practice with Real-World Datasets and Implementation of Machine Learning Algorithms
 |
| Implementation of Machine Learning Algorithms |
Introduction: The ability to apply Machine Learning algorithms to real-world datasets is a critical skill for a data scientist. In this chapter, we will go through a series of hands-on projects that will provide guided practice in implementing various Machine Learning algorithms and working with real-world datasets.
Project 1: Predicting House Prices
In this project, we will work with a dataset of house prices in Boston, Massachusetts. The goal of the project is to use a Linear Regression algorithm to predict the prices of houses in the area.
Step 1: Loading the Dataset The first step is to load the dataset into a Pandas DataFrame. The dataset can be downloaded from the UCI Machine Learning Repository.
 |
Project 1: Predicting House Prices
Step 2:
Exploratory Data Analysis Once the dataset has been loaded, the next step is to perform Exploratory Data Analysis (EDA) to gain insights into the data. This involves creating visualizations such as histograms and scatter plots to understand the distribution of the features and the relationship between features and the target variable (price).
Project 1: Predicting House Prices
 |
| Step 1 Output |
Step 3:
Preprocessing After completing EDA, we need to preprocess the data to prepare it for modeling. This involves handling missing values, transforming skewed variables, and scaling the features.
Step 4:
Model Selection and Training Next, we will select a Linear Regression model and train it on the preprocessed data. We will use scikit-learn library to perform this task.
Step 5:
Evaluation Finally, we will evaluate the performance of the model using metrics such as Mean Squared Error (MSE) and R-squared.
 |
| Mean Squared Error (MSE) |
0 Comments