Pokemon Total Hit Linear Regression Model

This model uses pytorch to create a predictive model of the total amount of attack hit a pokemon can deal. The features of this model are the type, defense, whether its a legendary or not and similar.

The data used was a text file (.csv) containing the characteristic of each pokemon. This is a database from Kaggle.

import torch
import torchvision
import torch.nn as nn
import pandas as pd
import seaborn as sns 
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
import warnings
warnings.filterwarnings('ignore')

data_df = pd.read_csv('/Users/ammaderazo/Documents/DS Bootcamp and Courses/Deep Learning with PyTorch/Project/Pokemon.csv')

data_df = data_df.drop('#', axis=1)

data_df.dtypes

Name          object
Type 1        object
Type 2        object
Total          int64
HP             int64
Attack         int64
Defense        int64
Sp. Atk        int64
Sp. Def        int64
Speed          int64
Generation     int64
Legendary       bool
dtype: object