Work in progress

RegressionModels/BostonHousing/predict_housing.py

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+
+"""
+Column	Meaning
+crim	Per capita crime rate by town
+zn	Proportion of residential land zoned for lots over 25,000 sq.ft.
+indus	Proportion of non-retail business acres per town
+chas	Charles River dummy variable (1 if tract bounds river, 0 otherwise)
+nox	Nitric oxides concentration (parts per 10 million)
+rm	Average number of rooms per dwelling
+age	Proportion of owner-occupied units built prior to 1940
+dis	Weighted distances to five Boston employment centers
+rad	Index of accessibility to radial highways
+tax	Full-value property-tax rate per $10,000
+ptratio	Pupil-teacher ratio by town
+black	1000(Bk - 0.63)^2, where Bk is the proportion of Black people by town
+lstat	% lower status of the population
+medv	Median value of owner-occupied homes in $1000’s (target variable)
+"""
+
+import pandas as pd
+import torch
+import torch.nn as nn
+
+df = pd.read_csv("./RegressionModels/BostonHousing/Boston.csv")
+
+
+# print(df.iloc[:,0:14])
+
+X = torch.tensor(df.iloc[:,0:14].values, dtype=torch.float32)
+Y = torch.tensor(df["medv"].values, dtype=torch.float32)
+
+model = torch.nn.Sequential(
+    torch.nn.Linear(14, 1)
+)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=5e-9)
+
+for epoch in range(2000):
+    predict_y = model(X)
+    loss = loss_fn(predict_y, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 99 ==0:
+        print(f'Epoch: {epoch}, Loss: {loss.item():.2f}')