Work in progress

RegressionModels/AdvertisementPrediction/advertising.csv

@@ -0,0 +1,201 @@
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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}')
+

RegressionModels/AutoMPG/auto-mpg.csv

@@ -0,0 +1,399 @@
+mpg,cylinders,displacement,horsepower,weight,acceleration,model year,origin,car name
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+15,8,429,198,4341,10,70,1,ford galaxie 500
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+14,8,340,160,3609,8,70,1,plymouth 'cuda 340
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+28.8,6,173,115,2595,11.3,79,1,chevrolet citation
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RegressionModels/AutoMPG/miles_per_gallon.py

@@ -0,0 +1,72 @@
+import pandas as pd
+import torch
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error
+import numpy as np
+
+df = pd.read_csv("./RegressionModels/AutoMPG/auto-mpg.csv")
+df = df.dropna(subset=df.columns)               #drop empty lines
+df = df[df['horsepower'] != '?']                #drop lines where horsepower is unknown
+df['horsepower'] = df['horsepower'].astype(int)  # convert object to int
+
+# print(df.dtypes)
+
+# print(df.iloc[:,1:8])
+
+
+X_train, X_test, Y_train, Y_test = train_test_split(df.iloc[:, 1:8], df["mpg"], test_size=0.2, random_state=42)     #split train and test data
+
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X_train.values)
+X = torch.tensor(X_scaled, dtype=torch.float32)
+
+scaler_Y = StandardScaler()
+Y_scaled = scaler_Y.fit_transform(Y_train.values.reshape(-1,1))
+Y = torch.tensor(Y_scaled, dtype=torch.float32)
+
+# X = torch.tensor(df.iloc[:, 1:8].values, dtype=torch.float32)
+# Y = torch.tensor(df["mpg"].values, dtype=torch.float32)
+
+model = torch.nn.Sequential(            #increas complexity with more neural network layers!
+    torch.nn.Linear(7, 10),
+    torch.nn.ReLU(),
+    torch.nn.Linear(10, 1)
+)
+
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+for epoch in range(3000):
+    pred_y = model(X)
+    loss = loss_fn(pred_y, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 999 == 0:
+        print(f"Epoch: {epoch}, Loss: {loss.item():.2f}")
+
+
+# Scale test features using the same scaler as training
+X_test_scaled = scaler.transform(X_test.values)
+X_test_tensor = torch.tensor(X_test_scaled, dtype=torch.float32)
+
+# Get predictions (still in scaled space)
+with torch.no_grad():
+    pred_Y_scaled = model(X_test_tensor).numpy()
+
+
+# Inverse-transform predictions and true Y values to original scale
+Y_test_array = Y_test.values.reshape(-1, 1)  # shape (n, 1)
+pred_Y = scaler_Y.inverse_transform(pred_Y_scaled)  # shape (n, 1)
+
+# Flatten to 1D for metrics
+Y_test_unscaled_flat = Y_test_array.ravel()
+pred_Y_flat = pred_Y.ravel()
+
+mae = mean_absolute_error(Y_test_unscaled_flat, pred_Y_flat)
+mape = np.mean(np.abs((Y_test_unscaled_flat - pred_Y_flat) / Y_test_unscaled_flat)) * 100
+print(f"Test MAE: {mae:.2f}")
+print(f"Test MAPE: {mape:.2f}%")

RegressionModels/BostonHousing/Boston.csv

@@ -0,0 +1,507 @@
+"","crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","black","lstat","medv"
+"1",0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
+"2",0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
+"3",0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
+"4",0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
+"5",0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
+"6",0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
+"7",0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
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+"465",7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
+"466",3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
+"467",3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
+"468",4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
+"469",15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
+"470",13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
+"471",4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
+"472",4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
+"473",3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
+"474",4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
+"475",8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
+"476",6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
+"477",4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
+"478",15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
+"479",10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
+"480",14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
+"481",5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
+"482",5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
+"483",5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
+"484",2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
+"485",2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
+"486",3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
+"487",5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
+"488",4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
+"489",0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
+"490",0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
+"491",0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
+"492",0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
+"493",0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
+"494",0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
+"495",0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
+"496",0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
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+"504",0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
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+"506",0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9

RegressionModels/BostonHousing/predict_housing.py

@@ -0,0 +1,46 @@
+
+"""
+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}')

RegressionModels/CaliforniaHousing/california_housing.py

@@ -0,0 +1,47 @@
+import pandas as pd
+import torch
+import torch.nn as nn
+from sklearn.preprocessing import StandardScaler
+
+df = pd.read_csv("./RegressionModels/CaliforniaHousing/housing.csv")
+
+
+df = df.dropna(subset=df.columns[:8])
+df['ocean_proximity_encoded'] = df['ocean_proximity'].astype('category').cat.codes     #Encodes text values as numerical ones
+
+# print(df)
+# print(df.iloc[:,0:8])
+
+scaler_x = StandardScaler()
+scaled_X = scaler_x.fit_transform(df.iloc[:,0:8].join(df["ocean_proximity_encoded"]).values)
+X = torch.tensor(scaled_X, dtype=torch.float32)
+
+scaler_y = StandardScaler()
+scaled_Y = scaler_y.fit_transform(df["median_house_value"].values.reshape(-1,1))
+Y = torch.tensor(scaled_Y, dtype=torch.float32)
+
+model = torch.nn.Sequential(
+    torch.nn.Linear(9, 18),
+    torch.nn.ReLU(),
+    torch.nn.Linear(18, 1)
+)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+X = X.to(device)
+Y = Y.to(device)
+
+
+for epoch in range(3000):
+    pred_y = model(X)
+    loss = loss_fn(pred_y, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 99 == 0:
+        print('Epoch: ', epoch, f"loss: {loss.item():.2f}")
+

RegressionModels/PredictSallary/Salary_dataset.csv

@@ -0,0 +1,31 @@
+,YearsExperience,Salary
+0,1.2000000000000002,39344.0
+1,1.4000000000000001,46206.0
+2,1.6,37732.0
+3,2.1,43526.0
+4,2.3000000000000003,39892.0
+5,3.0,56643.0
+6,3.1,60151.0
+7,3.3000000000000003,54446.0
+8,3.3000000000000003,64446.0
+9,3.8000000000000003,57190.0
+10,4.0,63219.0
+11,4.1,55795.0
+12,4.1,56958.0
+13,4.199999999999999,57082.0
+14,4.6,61112.0
+15,5.0,67939.0
+16,5.199999999999999,66030.0
+17,5.3999999999999995,83089.0
+18,6.0,81364.0
+19,6.1,93941.0
+20,6.8999999999999995,91739.0
+21,7.199999999999999,98274.0
+22,8.0,101303.0
+23,8.299999999999999,113813.0
+24,8.799999999999999,109432.0
+25,9.1,105583.0
+26,9.6,116970.0
+27,9.7,112636.0
+28,10.4,122392.0
+29,10.6,121873.0

RegressionModels/PredictSallary/salarypredictor.py

@@ -0,0 +1,34 @@
+import torch
+import pandas as pd
+import matplotlib.pyplot as plt
+
+file = './RegressionModels/PredictSallary/Salary_dataset.csv'
+df = pd.read_csv(file)
+
+X = torch.tensor(df["YearsExperience"].values, dtype=torch.float32).unsqueeze(1)
+y = torch.tensor(df['Salary'].values, dtype=torch.float32).unsqueeze(1) 
+
+model = torch.nn.Sequential(
+    torch.nn.Linear(1,1)
+)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
+
+# 5. Training loop
+for epoch in range(1000):
+    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}')
+
+# 6. Plot the results
+plt.scatter(X.numpy(), y.numpy(), label='Actual data')
+plt.plot(X.numpy(), model(X).detach().numpy(), color='red', label='Model prediction')
+plt.xlabel('Years of Experience')
+plt.ylabel('Salary')
+plt.legend()
+plt.show()