Ainaganiu/House-Price-Prediction-in-Nigeria

The objective of this hackathon is to create a powerful and accurate predictive model that can estimate the prices of houses in Nigeria. # House-Price-Prediction-in-Nigeria The objective of this project is to create a powerful and accurate predictive model that can estimate the prices of houses in Nigeria. #DSN and Microsoft Skills for Job Hackathon The objective of the challenge is to predict the price (Amount (Million Naira) the company should sell a house based on the available data ID Location Amount (Million Naira) -Bathroom Bedroom Parking space The objective is to predict the price. This dataset, consisting of 14,000 rows and 7 columns, reveals that the number of bathrooms and bedrooms significantly influences the price of a house, while the parking space has a minor influence unless it accommodates 5 or 6 vehicles. **Summary** A right-skewed histogram of housing prices indicates that the majority of houses are priced towards the lower end, with a long tail on the right side representing a smaller number of higher-priced properties. Most customers purchase houses with 1 to 4 bedrooms, which aligns with the mean value of 4.3 and a standard deviation of 2.4. Similarly, houses with 1 to 3 bathrooms are most commonly purchased, aligning with a mean value of 3.1 and a standard deviation of 1.9. Regarding parking space, houses with 3 to 4 spaces are most popular among customers, which aligns with the mean value of 3.2 and a standard deviation of 1.5. Such patterns are common in real estate markets, where the majority of properties fall within a certain price range, while a smaller number of high-end properties contribute to the right tail of the distribution. This project further used machine learning to predict house prices using Python Programming Language. Click here to see the codes Click here to see