You are provided with the weekly spends on different marketing channels of an XYZ company for a period from 2015 to 2019. The variables in the dataset are described as follows:
| Feature | Description |
|---|---|
| DATE | date in YYYY-MM-DD format |
| Sales | Weekly revenue figure made by the comapany |
| TV_Spends | Weekly spends on TV advertisements |
| OOH_Spends | Weekly spends on Outdoor Medium |
| Print_Spends | Weekly spends on Print Medium like TV and magazines |
| FB_Impressions | Weekly Impressions of ads on Facebook | impressions can be described as views that a particular ad received on facebook |
| FB_Spends | Weekly spends on Facebook Ads |
| Search_Spends | Weekly spends on paid search results. These are spends done to get the paid advertisements on the search results. |
| Paid_Search_Clicks | Weekly clicks on the paid advertisement |
| events | Events can be described as festivals or promotional events done by the company. This is a categorical variable. |
| competitor_sales_B | Sales figure of the competition product |
You are required to perform necessary data analysis/ EDA and extract meaningful insights from this dataset and finally build a model using this dataset to predict the sales.
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scipy
- Scikit-learn
- XGBoost
- CatBoost
- LightGBM
- Joblib
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent