Hello Sir,
I understand the problem statement. I am outlining the solution below:
1. Data Collection:
If you have the dataset before hand we can can skip this step otherwise we'll have to Collect a rich dataset encompassing relevant features.
2. Data Cleaning and Preprocessing:
Ensuring the dataset is cleaned and preprocessed to handle missing values, outliers, and format inconsistencies. Transform categorical variables into numerical representations and scale/normalize numerical features for uniformity.
3. Exploratory Data Analysis (EDA):
Perform exploratory data analysis to gain a deeper understanding of the data's distribution and relationships.
4. Model Selection:
Choosing suitable model, considering factors such as interpretability, complexity, and performance. Linear regression, decision trees, random forests, gradient boosting, and neural networks are potential candidates.
5. Model Training and Evaluation:
training the selected model on the training set, and evaluate its performance on the testing set using relevant metrics (e.g., Mean Absolute Error, Mean Squared Error, R-squared).
Thank you for considering this proposal. If you like my proposed solution we can connect to discuss further steps. I am available for further discussion to address any questions or concerns you may have.
Sincerely,
Nalin Verma