samarth-agrawal-86

# load packages
import pandas as pd 
from fast_ml.utilities import display_all
from fast_ml.feature_selection import get_duplicate_features

# load dataset
df = pd.read_csv('/kaggle/input/dataset-1/dataset_1.csv')

# function to detect duplicate features
duplicate_features = get_duplicate_features(df)

samarth-agrawal-86

# load packages
import pandas as pd 
from fast_ml.utilities import display_all
from fast_ml.feature_selection import get_duplicate_features

# load dataset
df = pd.read_csv('/kaggle/input/dataset-1/dataset_1.csv')

# function to detect duplicate features
duplicate_features = get_duplicate_features(df)

samarth-agrawal-86

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)

# Let's say we want to split the data in 80:10:10 for train:valid:test dataset
train_size = 0.8
valid_size=0.1

train_index = int(len(df)*train_size)

samarth-agrawal-86

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)


from fast_ml.model_development import train_valid_test_split

X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(df, target = 'SalePrice', 
                                                                            method='sorted', sort_by_col='saledate',
                                                                            train_size=0.8, valid_size=0.1, test_size=0.1)

samarth-agrawal-86

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)


from fast_ml.model_development import train_valid_test_split

X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(df, target = 'SalePrice', 
                                                                            train_size=0.8, valid_size=0.1, test_size=0.1)

samarth-agrawal-86

import pandas as pd

df = pd.read_csv('/kaggle/input/bluebook-for-bulldozers/TrainAndValid.csv', parse_dates=['saledate'], low_memory=False)

from sklearn.model_selection import train_test_split

# Let's say we want to split the data in 80:10:10 for train:valid:test dataset
train_size=0.8

X = df.drop(columns = ['SalePrice']).copy()

samarth-agrawal-86

# Get test data loss and accuracy

test_losses = [] # track loss
num_correct = 0

# init hidden state
h = net.init_hidden(batch_size)

net.eval()
# iterate over test data

samarth-agrawal-86

from string import punctuation

def tokenize_review(test_review):
    test_review = test_review.lower() # lowercase
    # get rid of punctuation
    test_text = ''.join([c for c in test_review if c not in punctuation])

    # splitting by spaces
    test_words = test_text.split()

samarth-agrawal-86

# loss and optimization functions
lr=0.001

criterion = nn.BCELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)


# training params

epochs = 4 # 3-4 is approx where I noticed the validation loss stop decreasing

samarth-agrawal-86

import torch.nn as nn

class SentimentLSTM(nn.Module):
    """
    The RNN model that will be used to perform Sentiment analysis.
    """

    def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
        """
        Initialize the model by setting up the layers.