California-housing-prices

Considering different attributes of housing predicting its price.

Using linera regression model trying to prdict housing price in california

Here the dataset is used from the kaggel

Overview of notebook

Define Objective: What you want to do ?

Gist of a datasets:
Datatypes of attributes.
Wether continuous or decrete.
finding stats such as min, max, std, median and so on.
Visulize the dataset.
Datatypes of attributes

Define training and testing datasets per usecase e.g. random vs stratified.

Correlation analysis(pairwise and attribute correlation).

Data quality: clean the dataset (Missing values, errors, duplication, outliers etc.).

Data trasnformation (Like normalizing, standardization, onehot code enoder etc.).

Quickly define a model and see how it permorms: Base Model.

Define model accorindig to application (ML models, DNN, CNN, RNN, etc.).

Hyperparmaneters tuning on the validation set.

Carry-out different test scores(Prcision, Recall, ROC, PR, etc.) on the test set.

Deploy the model and use it to predict real life scenarios.