1 files changed, 51 insertions, 0 deletions
diff --git a/scatter_plot.py b/scatter_plot.py
new file mode 100644
index 0000000..8cdfea2
--- /dev/null
+++ b/scatter_plot.py
@@ -0,0 +1,51 @@
+from module.constants import (
+    HOUSE_COLORS,
+    NUMERICAL_FEATURE_CSV_TITLES,
+    HOUSE_FEATURE_CSV_TITLE,
+)
+from module.dataset_manip import parse_csv
+import matplotlib.pyplot as plt
+import os
+import pandas as pd
+import sys
+
+if len(sys.argv) < 2:
+    print(f"Usage: python {__file__} <dataset.csv>")
+    exit(-1)
+
+# Get data from CSV file
+dataset_filename = sys.argv[1]
+data = parse_csv(
+    dataset_filename, NUMERICAL_FEATURE_CSV_TITLES, [HOUSE_FEATURE_CSV_TITLE]
+)
+df = pd.DataFrame(data)
+
+# Show a scatter plot for each combination of numerical feature
+numerical_feature_count = len(NUMERICAL_FEATURE_CSV_TITLES)
+for i in range(numerical_feature_count):
+    for j in range(i + 1, numerical_feature_count):
+        x_axis_feature = NUMERICAL_FEATURE_CSV_TITLES[i]
+        y_axis_feature = NUMERICAL_FEATURE_CSV_TITLES[j]
+        title = f"{x_axis_feature} vs {y_axis_feature} Scatter Plot"
+        fig, ax = plt.subplots()
+        for house in df[HOUSE_FEATURE_CSV_TITLE].dropna().unique():
+            house_df = df.loc[df[HOUSE_FEATURE_CSV_TITLE] == house]
+            ax.scatter(
+                house_df.loc[:, x_axis_feature],
+                house_df.loc[:, y_axis_feature],
+                alpha=0.8,
+                color=HOUSE_COLORS[house.lower()],
+                label=house,
+                s=10,
+            )
+        ax.set_title(title)
+        ax.set_xlabel(f"{x_axis_feature} Score")
+        ax.set_ylabel(f"{y_axis_feature} Score")
+        ax.legend()
+
+        # Save to png file
+        os.makedirs("output/scatter_plot", exist_ok=True)
+        save_filename = f"output/scatter_plot/{title}.png"
+        fig.savefig(save_filename)
+        plt.close(fig)
+        print(f"Saved {title} to {save_filename}")