Update pages/EDA.py

pages/EDA.py

@@ -462,4 +462,56 @@ st.markdown("""
     2. Requires very little water and nutrients, making it cost-effective.
     3. Has a stable market demand
 </div>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)
+
+
+
+
+# **Scatter Plot: Rainfall vs. Crop**
+st.subheader(" Rainfall vs. Crop")
+fig, ax = plt.subplots(figsize=(5, 3))  # Reduced size
+sns.scatterplot(data=df, x="Rainfall", y="Crop", hue="Crop", palette="rainbow", legend=False, ax=ax)
+ax.set_xlabel("Rainfall (mm)", fontsize=8)
+ax.set_ylabel("Crop", fontsize=8)
+ax.set_title("Rainfall vs Crop Type", fontsize=10)
+st.pyplot(fig)
+
+# **Scatter Plot: Temperature vs. Humidity**
+st.subheader("Temperature vs. Humidity")
+fig, ax = plt.subplots(figsize=(5, 3))  # Reduced size
+sns.scatterplot(data=df, x="Temperature", y="Humidity", alpha=0.6, color="blue", ax=ax)
+ax.set_xlabel("Temperature (°C)", fontsize=8)
+ax.set_ylabel("Humidity (%)", fontsize=8)
+ax.set_title("Temperature vs Humidity", fontsize=10)
+st.pyplot(fig)
+
+# **Heatmap: Correlation Between Soil & Climate Factors**
+st.subheader("Correlation Heatmap of Soil & Climate Factors")
+corr_matrix = df[['Nitrogen', 'Phosphorus', 'Potassium', 'Temperature', 'Humidity', 'pH_Value', 'Rainfall']].corr()
+fig, ax = plt.subplots(figsize=(5, 3))  # Reduced size
+sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", ax=ax)
+ax.set_title("Correlation Heatmap", fontsize=10)
+st.pyplot(fig)
+
+# **Crop Recommendations Based on Season**
+st.subheader("🌾 Crop Recommendations by Season")
+
+# **Define Seasonal Conditions**
+summer_conditions = (df["Temperature"].between(20, 40)) & (df["Rainfall"].between(50, 200)) & (df["Humidity"].between(40, 70))
+rainy_conditions = (df["Temperature"].between(20, 30)) & (df["Rainfall"] > 200) & (df["Humidity"].between(65, 95))
+winter_conditions = (df["Temperature"].between(10, 20)) & (df["Rainfall"] < 150) & (df["Humidity"].between(30, 70))
+
+# **Get Unique Crops for Each Season**
+summer_crops = df[summer_conditions]["Crop"].unique().tolist()
+rainy_crops = df[rainy_conditions]["Crop"].unique().tolist()
+winter_crops = df[winter_conditions]["Crop"].unique().tolist()
+
+# **Display Crop Recommendations**
+st.markdown("### ☀ Summer Crops")
+st.write(", ".join(summer_crops) if summer_crops else "No crops suitable for summer.")
+
+st.markdown("### 🌧 Rainy Season Crops")
+st.write(", ".join(rainy_crops) if rainy_crops else "No crops suitable for the rainy season.")
+
+st.markdown("### ❄ Winter Crops")
+st.write(", ".join(winter_crops) if winter_crops else "No crops suitable for winter.")