Home Price Prediction: A Machine Learning Analysis Using Linear Regression

In machine learning, algorithms empower computers to learn from data autonomously, facilitating predictions or decisions without explicit programming. Linear regression, a statistical method, elucidates the correlation between independent and dependent variables by fitting a straight line to the dataset. In this blog, I will explain my analysis using a house prediction dataset from Kaggle. This blog is specifically about exploring and experimenting. Let's get started.

Introduction:

Predicting home prices accurately is paramount in real estate, aiding buyers, sellers, and agents in making informed decisions. Leveraging machine learning algorithms, I have tried to forecast home prices based on various features such as area, bathrooms, bedrooms, and more. This project utilizes linear regression analysis to uncover correlations and unveil insights from the dataset.

Data Extraction and Preparation

Data Set Information and Background:

In this dataset, my main objective is to predict the price of homes based on various features. These features include Area, Bathrooms, BHK (Bedrooms, Hall, Kitchen), Bedrooms, Status, Transaction, Locality, per sqft (Price per Square Foot), Parking, and Type. By digging into the data and understanding the correlations between different features, I aim to develop predictive models that accurately forecast home prices.

Data Preparation:

To kickstart my machine learning journey and forecast home prices using Python, I need to set up my environment by importing essential libraries. These libraries have predefined functions and tools that streamline the data analysis process. Here's a rundown of the key libraries I'll be using:

·       Pandas: For data manipulation and analysis.

·       NumPy: For numerical computing and array operations.

·       Matplotlib: For data visualization and plotting.

·       Seaborn: For statistical data visualization.

·       Scikit-learn: For machine learning algorithms and tools.

I have used the house.info() function to summarize my dataset, including key details about its dimensions and data types.

The dataset contains 1259 rows and 11 columns.

Plotting Histograms:

I have used histograms to visualize data distributions and uncover trends within the dataset. By plotting histograms, I aimed to gain insights into the underlying distribution of key features and reveal patterns that could enhance my understanding of the dataset.

I created six histograms as below, focusing specifically on features with numerical values. These histograms provided a visual representation of each feature's distribution of data points. Histograms proved instrumental in identifying outliers, comprehending the range of values for each feature, and detecting any underlying patterns or anomalies present in the data.

Performance Measures:

I needed to choose an appropriate evaluation strategy to gauge our model's effectiveness. This involved dividing the dataset into distinct parts. Initially, I split the data into two subsets: the training and testing sets. Typically, this split follows an 80-20 ratio, where the model trains on 80% of the data and tests its performance on the remaining 20%.

Purpose of Data Splitting:

The purpose of this division is crucial. I ensure that the model is tested on unseen data by training it on a subset of the data and evaluating its performance on another subset. Through this process, I gain insights into potential issues such as overfitting or underfitting, ensuring the model's reliability and effectiveness in making accurate predictions.

Feature scaling is essential before applying the model to the data. This process ensures that the data is in a consistent format, which is advisable for accurate model training and performance evaluation. Feature scaling can be achieved using methods such as standard scaling and normalization, both of which are available in the sci-kit-learn library.

Data Modeling

After completing the data cleaning process, I identified valuable features from the dataset that could be utilized to construct an appropriate model for training and predicting outcomes. Initially, it was necessary to define the problem type since the dataset comprised both input (features) and output (labels).

Consequently, I opted for an algorithm that falls under supervised learning. There are two main types of regression algorithms within this category: linear regression, typically employed for predicting continuous values, and logistic regression, utilized for classification tasks involving distinct values.

Evaluating Model Performance

RMSE, or Root Mean Square Error, is a metric used to assess an algorithm's accuracy. It represents the square root of the Mean Squared Error (MSE) value. In practical terms, RMSE measures the average magnitude of the difference between actual and predicted values. By comparing the actual and predicted values, we compute the MSE, which is the square of the difference between these values. Consequently, taking the square root of the MSE yields the RMSE value.

The below screenshot shows that we obtained a higher RMSE value. This indicates the necessity of employing a model capable of providing more accurate results.

Causes of Low Performance: There are primarily two factors that contribute to the underperformance of a machine learning algorithm:

• Underfitting Model: An underfitting model occurs when the selected dataset is insufficient and does not adequately represent the complexity of the underlying data. To address this issue, we can expand the dataset by adding additional columns to capture more features. Alternatively, employing a more sophisticated algorithm or reducing unnecessary data from the dataset can mitigate underfitting. Random Forest Regression is an algorithm known for yielding better results and accommodating nonlinear data.
  

• Overfitting Model: Conversely, an overfitting model results from excessive irrelevant data, reducing model performance. Overfitting may occur when using a complex algorithm with a simple dataset, resulting in inaccurate predictions. To mitigate overfitting, it's essential to choose the appropriate algorithm based on the dataset's characteristics or remove unnecessary rows and columns. Additionally, outliers can contribute to overfitting, making it crucial to address them appropriately. The Decision Tree algorithm is particularly susceptible to overfitting and requires careful handling to prevent it.

Conclusion:

In conclusion, my journey into predicting home prices through machine learning has illuminated the intricacies of real estate data analysis. I uncovered vital trends and patterns within the dataset through meticulous data preparation and visualization techniques like histograms, enhancing my understanding. Performance evaluation, including data splitting and RMSE analysis, allowed me to assess model accuracy and address challenges like underfitting and overfitting, ensuring robust predictions. My analysis provides a holistic framework for leveraging machine learning in real estate, enabling informed decision-making and insights into complex data dynamics.