diff --git a/custom_definitions.py b/custom_definitions.py
index b331731..6695b6c 100644
--- a/custom_definitions.py
+++ b/custom_definitions.py
@@ -19,3 +19,64 @@
 # Your code
 #==============Your Name=====================
 #Dont remove the following snippet and follow the same
+
+
+#==================================================shrihari====================================================================
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.preprocessing import OrdinalEncoder
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.pipeline import Pipeline
+
+
+class ST_DiamondFeatureEngineer(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        self.cut_order = ['Fair', 'Good', 'Very Good', 'Premium', 'Ideal']
+        self.color_order = ['J', 'I', 'H', 'G', 'F', 'E', 'D']
+        self.clarity_order = ['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF']
+        self.ordinal_encoder = OrdinalEncoder(categories=[self.cut_order, self.color_order, self.clarity_order])
+        self.numerical_features = []
+        self.categorical_cols = ['cut_grade', 'color_grade', 'clarity_grade']
+
+    def fit(self, X, y=None):
+        
+        df = X.copy()
+        
+        self.ordinal_encoder.fit(df[self.categorical_cols])
+        
+        self.numerical_features = df.select_dtypes(include=np.number).columns.drop(self.categorical_cols, errors='ignore')
+        return self
+
+    def transform(self, X, y=None):
+        df = X.copy()
+
+        if 'price_per_carat' in df.columns:
+            df = df.drop(columns=['price_per_carat'])
+
+        df[self.categorical_cols] = self.ordinal_encoder.transform(df[self.categorical_cols])
+
+        for col in self.numerical_features:
+            if col in df.columns:
+                Q1 = df[col].quantile(0.25)
+                Q3 = df[col].quantile(0.75)
+                IQR = Q3 - Q1
+                lower_bound = Q1 - 1.5 * IQR
+                upper_bound = Q3 + 1.5 * IQR
+                df[col] = df[col].clip(lower_bound, upper_bound)
+
+        df['density'] = df['carat_weight'] / (df['volume_mm3'] + 0.0001)
+        df['depth_per_width'] = df['depth_percentage'] / (df['width_mm'] + 0.0001)
+        df['carat_x_clarity'] = df['carat_weight'] * (df['clarity_grade'] + 1)
+        df['carat_x_color'] = df['carat_weight'] * (df['color_grade'] + 1)
+        df['carat_x_cut'] = df['carat_weight'] * (df['cut_grade'] + 1)
+        
+        return df
+    
+
+submission_pipeline = Pipeline(steps=[
+    ('feature_engineer', ST_DiamondFeatureEngineer()),
+    ('model', RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1))
+])
+#==============================shrihari=====================================================================================
\ No newline at end of file