Work in progress

2025-09-29 08:59:47 +02:00
parent 8a2a9d1064
commit b93df4af0f
26 changed files with 22986 additions and 0 deletions
--- a/RegressionModels/AdvertisementPrediction/advertising.csv
+++ b/RegressionModels/AdvertisementPrediction/advertising.csv
@ -0,0 +1,201 @@
+TV,Radio,Newspaper,Sales
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--- a/RegressionModels/AdvertisementPrediction/predict_sales.py
+++ b/RegressionModels/AdvertisementPrediction/predict_sales.py
@ -0,0 +1,25 @@
+import pandas as pd
+import torch
+import torch.nn as nn
+
+df = pd.read_csv("./RegressionModels/AdvertisementPrediction/advertising.csv")
+
+X = torch.tensor(df[["TV", "Radio", "Newspaper"]].values, dtype=torch.float32)
+Y = torch.tensor(df["Sales"].values, dtype=torch.float32)
+
+model = torch.nn.Sequential(
+    torch.nn.Linear(3,1)
+)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=3e-5)
+
+for epoch in range(2000):
+    y_pred = model(X)
+    loss = loss_fn(y_pred, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 100 == 99:
+        print(f'Epoch {epoch+1}, Loss: {loss.item():.2f}')
+