Update pages/EDA.py

pages/EDA.py

@@ -129,13 +129,41 @@ feature_mapping = {
     "pH Value Distribution per Crop": "pH_Value",
     "Rainfall Distribution per Crop": "Rainfall",
 }
+viz_option = st.selectbox("Select a feature visualization:", options=list(feature_mapping.keys()))
 
+
+
+
+# Feature Visualization
+feature_mapping = {
+    "Nitrogen Requirement per Crop": "Nitrogen",
+    "Phosphorus Requirement per Crop": "Phosphorus",
+    "Potassium Requirement per Crop": "Potassium",
+    "Temperature Distribution": "Temperature",
+    "Humidity Distribution": "Humidity",
+    "pH Value Distribution": "pH_Value",
+    "Rainfall Distribution": "Rainfall",
+}
+viz_option = st.select_slider("Select a feature visualization:", options=list(feature_mapping.keys()))
+
+# Function to plot feature distribution
+def plot_feature(feature, title):
+    fig, ax = plt.subplots(figsize=(6, 3))  # Reduced plot size
+    sns.histplot(df[feature], bins=20, kde=True, ax=ax, color="skyblue")
+    ax.set_title(title)
+    ax.set_xlabel(feature)
+    ax.set_ylabel("Density")
+    st.pyplot(fig)
+
+# Display selected feature plot
 plot_feature(feature_mapping[viz_option], viz_option)
 
+# Pie Chart for Crop Proportions (Displayed Separately)
+st.subheader("📊 Crop Proportions")
 crop_counts = df['Crop'].value_counts()
-explode = [0.1 if i == 0 else 0 for i in range(len(crop_counts))]  # Highlighting the most frequent crop
+explode = [0.1 if i == 0 else 0 for i in range(len(crop_counts))]
 
-fig, ax = plt.subplots(figsize=(8, 4))  # Adjusted size
+fig, ax = plt.subplots(figsize=(6, 3))  # Reduced plot size
 ax.pie(
     crop_counts, labels=crop_counts.index, autopct='%1.1f%%',
     startangle=140, explode=explode, colors=sns.color_palette("rainbow", len(crop_counts))
@@ -143,13 +171,10 @@ ax.pie(
 ax.set_title("Proportion of Crops")
 st.pyplot(fig)
 
+# Crop Recommendations
 st.markdown("### 🌾 Crop Recommendations Based on Soil Nutrients & Climate Factors")
 st.markdown(
-    "<p style='color:green; font-size:16px;'>✔ Crops that require above-average soil nutrients & climate factors</p>", 
-    unsafe_allow_html=True
-)
-st.markdown(
-    "<p style='color:blue; font-size:16px;'>✔ Crops that require below-average soil nutrients & climate factors</p>", 
+    "<p style='color:green; font-size:16px;'>✔ Crops that require above-average soil nutrients & climate factors: Banana, Rice, Papaya, Jute.</p>", 
     unsafe_allow_html=True
 )
 
@@ -167,7 +192,7 @@ avg_potassium = df['Potassium'].mean()
 # Function to plot bar charts
 def plot_nutrient_crops(data, title):
     crop_counts = data.value_counts()
-    fig, ax = plt.subplots(figsize=(8, 4))  # Decreased plot size
+    fig, ax = plt.subplots(figsize=(6, 3))  # Further reduced plot size
     crop_counts.plot(kind='bar', color='skyblue', ax=ax)
     ax.set_xlabel('Crops')
     ax.set_ylabel('Count')
@@ -183,14 +208,17 @@ if viz_option == "Crops in Nutrient-Rich Soil":
     ]['Crop']
     plot_nutrient_crops(more_avg_of_soil_nutrients, "Crops Growing in Nutrient-Rich Soil")
     st.markdown(
-        "<p style='color:green; font-size:16px;'>✔ Insights: Banana is the most frequent crop, indicating it requires high soil nutrients.</p>", 
+        "<p style='color:green; font-size:18px;'>1.  Banana is the most frequent crop, indicating it requires high soil nutrients.</p>", 
         unsafe_allow_html=True
     )
     st.markdown(
-        "<p style='color:green; font-size:16px;'>✔ Rice, Papaya, and Jute also need good nutrients but less than Banana.</p>", 
+        "<p style='color:green; font-size:18px;'>2.  Rice, Papaya, and Jute also need good nutrients but less than Banana.</p>", 
         unsafe_allow_html=True
     )
-
+  st.markdown(
+    "<p style='color:blue; font-size:16px;'>✔ Crops that require below-average soil nutrients & climate factors: Orange, Mango, Coconut.</p>", 
+    unsafe_allow_html=True
+)
 elif viz_option == "Crops in Nutrient-Poor Soil":
     less_avg_of_soil_nutrients = df[
         (df['Nitrogen'] < avg_nitrogen) & 
@@ -198,11 +226,21 @@ elif viz_option == "Crops in Nutrient-Poor Soil":
         (df['Potassium'] < avg_potassium)
     ]['Crop']
     plot_nutrient_crops(less_avg_of_soil_nutrients, "Crops Growing in Nutrient-Poor Soil")
-    st.markdown(
-        "<p style='color:blue; font-size:16px;'>✔ Insights: Orange, Mango, and Coconut are the most frequent crops found in nutrient-poor soil.</p>", 
-        unsafe_allow_html=True
-    )
-    st.markdown(
-        "<p style='color:blue; font-size:16px;'>✔ These crops are well-adapted to low-fertility conditions and can grow even when nutrient levels are below average.</p>", 
-        unsafe_allow_html=True
-    )
+# Additional Insights Section
+st.markdown("<h2 style='color: #2E86C1; font-size: 23px;'> Insights :</h2>", unsafe_allow_html=True)
+st.markdown("""
+<style>
+    .insights-box {
+        font-size: 18px;  /* Decreased font size */
+        color: #333;
+        background-color: #f9f9f9;
+        padding: 10px;
+        border-radius: 10px;
+        border-left: 5px solid #2E86C1;
+    }
+</style>
+<div class='insights-box'>
+    1. Orange, Mango, and Coconut are the most frequent crops found in nutrient-poor soil.<br>
+    2. These crops are well-adapted to low-fertility conditions and can grow even when nutrient levels are below average.
+</div>
+""", unsafe_allow_html=True)