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prediction.py

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+import torch
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+import torch.nn as nn
+import torch.nn.functional as F
+
+class ANN_Model(nn.Module):
+    def __init__(self,input_features=82,hidden1=20,hidden2=20,out_features=3):
+        super().__init__()
+        self.f_connected1=nn.Linear(input_features,hidden1)
+        self.f_connected2=nn.Linear(hidden1,hidden2)
+        self.out=nn.Linear(hidden2,out_features)
+    def forward(self, x):
+        x=F.relu(self.f_connected1(x))
+        x=F.relu(self.f_connected2(x))
+        x=self.out(x)
+        return x
+
+data = pd.read_csv("Sales.csv")
+
+data["Profit_Category"] = pd.cut(data["Profit"], bins=[-np.inf, 500, 1000, np.inf], labels=[0, 1, 2])
+bike = data.loc[:, ['Customer_Age', 'Customer_Gender', 'Country','State', 'Product_Category', 'Sub_Category', 'Profit_Category']] 
+bikes = pd.get_dummies(bike, columns=['Country', 'State', 'Product_Category', 'Sub_Category', 'Customer_Gender'])
+X = bikes.drop('Profit_Category', axis=1).values
+y = bikes['Profit_Category'].values
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+scaler = StandardScaler()
+X_test = scaler.fit_transform(X_test)
+
+X_test = torch.FloatTensor(X_test)
+y_test = torch.LongTensor(y_test)
+
+model = torch.load("classificationn_model.pt")
+model.eval()  
+
+with torch.no_grad():
+    y_pred = model(X_test)
+
+_, predicted = torch.max(y_pred.data, 1)
+
+np.savetxt("predictions1.txt", predicted.numpy(), fmt='%d')