https://github.com/bilalhassankhan007/compwallah_april-2024

Internship Tasks
https://github.com/bilalhassankhan007/compwallah_april-2024

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Internship Tasks

• Host: GitHub
• URL: https://github.com/bilalhassankhan007/compwallah_april-2024
• Owner: bilalhassankhan007
• Created: 2024-03-21T18:51:23.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-03-24T09:23:04.000Z (9 months ago)
• Last Synced: 2024-03-24T10:27:27.885Z (9 months ago)
• Language: Jupyter Notebook
• Size: 335 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
**Problem Statement:** 

The task at hand involves predicting house prices based on a given dataset. This is a regression problem, where the objective is to develop models that accurately estimate the selling prices of houses using various features provided in the dataset.

**Approaches Utilized:**

1. **Data Cleaning and Preprocessing:** Initially, the dataset underwent thorough cleaning to handle null values and ensure data quality. This step involved techniques such as imputation and removal of inconsistent or irrelevant data.

   

2. **Model Selection:** Two machine learning algorithms, namely Support Vector Machine (SVM) and Linear Regression, were chosen for building predictive models. These algorithms were selected due to their effectiveness in regression tasks and their ability to capture complex relationships within the data.

4. **Evaluation Metrics:** To assess the performance of the models, standard regression evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were employed. These metrics provide insights into the accuracy and precision of the predictions made by the models.

5. **Exploratory Data Analysis (EDA):** EDA techniques, including correlation analysis using heatmaps and visualization using barplots, were utilized to gain insights into the relationships between different features and the target variable (house prices). This step helped in identifying significant predictors and understanding the data distribution.

6. **Feature Encoding:** Categorical data present in the dataset were encoded using OneHotEncoder to convert them into numerical format. This encoding ensures compatibility with machine learning algorithms that require numerical input.

7. **Training and Testing:** The dataset was split into training and testing sets to train the models on a subset of the data and evaluate their performance on unseen data. This step helps in assessing the generalization capabilities of the models.

**Conclusion:**

Upon evaluating the performance of both SVM and Linear Regression models, it was observed that the SVM model provided higher accuracy in predicting house prices. This conclusion was drawn based on the comparison of evaluation metrics such as RMSE and MAE. The predictive power of the SVM model makes it a suitable choice for accurate house price estimation in this context.