This dataset is related to houses for sale collected from various real estate agencies. These elements are features of each home which is of interest to potential property buyers. The file includes 26 variables, which are described in detail in the variable view tab. The aim of the research is to create a model for the sale price of the property and to find relationships between the rest of the property's characteristics.
The primary objective is to explore this dataset using various data analysis techniques, including descriptive statistics, exploratory analysis, correlation analysis, and regression modeling. Specifically, we employ linear regression and k-nearest neighbors (KNN) regression models to predict housing prices, utilizing separate training and test datasets and make a comparison of them.
Dataset: http://www.amstat.org/publications/jse/v19n3/decock/DataDocumentation.txt

1. Descriptive Statistics

• Summary Statistics: Calculating measures such as mean, median, mode, standard deviation, and range to describe the central tendency and spread of the data.
• Data Visualization: Generating visual representations, such as histograms, box plots, and scatter plots, to explore the distribution of individual features and identify any outliers or anomalies.

2. Exploratory Analysis

• Feature Exploration: Investigating the characteristics of individual features and their potential impact on housing prices.
• Pattern Recognition: Identifying trends, patterns, and relationships within the dataset through visualizations and statistical analysis.
• Data Transformation: Preparing the data for modeling by handling missing values, encoding categorical variables, and scaling numerical features.

3. Exploratory Analysis

• Feature Exploration: Investigating the characteristics of individual features and their potential impact on housing prices.
• Pattern Recognition: Identifying trends, patterns, and relationships within the dataset through visualizations and statistical analysis.
• Data Transformation: Preparing the data for modeling by handling missing values, encoding categorical variables, and scaling numerical features.

4. Correlation Analysis

• Correlation Coefficients: Calculating correlation coefficients, such as Pearson correlation, to quantify the strength and direction of relationships between variables.
• Correlation Heatmaps: Visualizing correlations among features using heatmaps to identify highly correlated variables and potential multicollinearity issues.

5. Regression Modeling

• Linear Regression: Building a linear regression model to predict housing prices based on a set of independent variables.
• Train-Test Split: Dividing the dataset into training and testing sets to train the model on a subset of the data and evaluate its performance on unseen data. -Model Evaluation: Assessing the model's performance using metrics such as mean squared error (MSE), R-squared, and root mean squared error (RMSE).
• K-Nearest Neighbors (KNN) Regression: Implementing a KNN regression model to predict housing prices by considering the similarity between a given data point and its k nearest neighbors in the feature space.

The analysis of the Ames Housing dataset using descriptive statistics, exploratory analysis, correlation analysis, and regression modeling techniques provides valuable insights into the factors influencing housing prices in Ames, Iowa. By employing linear regression and KNN regression models, we aim to accurately predict housing prices and contribute to a better understanding of the real estate market dynamics in the region.