Task 1: Import Libraries and Display Basic Information Overview: We start by importing essential libraries for data analysis and setting the display option to show all columns. The dataset is read, and the first 100 rows, along with column information, are displayed for initial exploration.
Insights: The dataset contains various columns with different data types. The first 100 rows give a glimpse of the dataset.
Task 2: Missing Values and Numerical Features Analysis Overview: We delve into missing values and analyze numerical features. This includes identifying columns with missing values, visualizing their impact on the target variable (SalePrice), and exploring numerical feature distributions.
Insights: Several columns have missing values, requiring further attention. The relationships between numerical features and SalePrice are explored through boxplots. Discrete and continuous variables are investigated for their influence on SalePrice.
Task 3: Outliers and Categorical Features Analysis Overview: This task involves detecting outliers, exploring the relationship between categorical features and SalePrice, and understanding correlations between numerical features and SalePrice. Feature engineering steps are outlined to handle missing values, categorical and numerical variables, and temporal variables.
Insights: Outliers may impact the model and should be considered during preprocessing. Categorical features play a role in determining SalePrice. Correlations between numerical features and SalePrice are analyzed. Feature engineering steps are crucial for preparing data for machine learning models. These insights serve as a foundation for deeper analysis and can guide further steps in your house price prediction project. You can expand on these insights based on your observations and additional analyses.