Includes data preprocessing steps, merging dataframes, PCA analysis, data distribution visualization (demonstarted using 3 columns & box plot). The dataset is collected from Kaggle. The dataset is split into train and test files into .csv format. I have merged both .csv files to perform preprocessing tasks. Handling duplicate and null values are basic steps. At first I have removed duplicates and null values but later on imputation methods has been introduced to remove missing values. KNNImputer is used to perform the task.
PCA Analysis is dimensionality reduction technique and I reduced the dimensionality into 3D.
Dataset Link: https://www.kaggle.com/datasets/salauddintapu/house-prices-advanced-regression-techniques
This ipynb file is for image preprocessing step. It can will read all the image files of a given directory. Initially the file will store the folder name and image name into two different lists and after doing so, it will create a python dictionary using these lists and finally a pandas dataframe. After creating a df, the images will be encoded acording to the folder label using LabelEncoder. And in the end, the program will provide a csv file containg all these informations.