Problem statement

You work in an event management company. On Mother's Day, your company has organized an event where they want to cast positive Mother's Day related tweets in a presentation. Data engineers have already collected the data related to Mother's Day that must be categorized into positive, negative, and neutral tweets.

You are appointed as a Machine Learning Engineer for this project. Your task is to build a model that helps the company classify these sentiments of the tweets into positive, negative, and neutral.

Data description

This data set consists of six columns:

The data folder consists of two .csv files. The details are as follows:

• train.csv: 3235 x 6
• test.csv: 1387 x 5

Submission format

You are required to write your predictions in a .csv file and upload it by clicking the Upload File button.

Sample submission

id,sentiment_class
1.24502457848689e+18,0
1.24575874656913e+18,0
1.24608735665184e+18,-1
1.24480262804494e+18,0
1.24487627472153e+18,-1

Evaluation metric

$score = {100* f1\_score(actual\_values,predicted\_values,average = 'weighted')}$

Note: To avoid any discrepancies in the scoring, ensure all the index column (id) values in the submitted file match the values in the provided test.csv file.