diff --git a/school_mapping.py b/school_mapping.py
new file mode 100644
index 0000000..dd6ea07
--- /dev/null
+++ b/school_mapping.py
@@ -0,0 +1,87 @@
+import pandas as pd
+from rapidfuzz import fuzz, process
+from indic_transliteration import sanscript
+
+# Load data
+source_a = pd.read_csv('data/school_list_A.tsv', sep='\t')
+source_b = pd.read_csv('data/school_list_B.tsv', sep='\t')
+
+# Function to transliterate Devanagari text to Romanized text using Velthuis method
+def transliterate_text(text):
+    return sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.VELTHUIS)
+
+# Clean and normalize data
+source_a['velthuis'] = source_a['school'].apply(lambda x: transliterate_text(x)).str.lower().str.strip()
+source_a['district1'] = source_a['district1'].str.lower().str.strip()
+source_b['district'] = source_b['district'].str.lower().str.strip()
+
+# Create a dictionary for district name to district id mapping in Source B
+district_mapping_b = source_b[['district', 'district_id']].drop_duplicates().set_index('district')['district_id'].to_dict()
+
+# Create a dictionary for district name to district id mapping in Source A (assuming jilla.tsv contains this mapping)
+# Assuming 'जिल्ला' is the Devanagari name for 'district' in jilla.tsv
+jilla = pd.read_csv('data/jilla.tsv', sep='\t')
+district_mapping_a = jilla.set_index('जिल्ला')['district_id'].to_dict()
+
+# Function to match schools based on transliteration and district
+def match_schools(source_a, source_b, district_mapping_a, district_mapping_b, threshold=70):
+    matches = []
+    
+    for index, row in source_a.iterrows():
+        school_id_a = row['school_id']
+        velthuis_name = row['velthuis']
+        district_a = row['district1']
+        
+        # Get district id from district name in Source A
+        district_id_a = district_mapping_a.get(district_a)
+        
+        if district_id_a is not None:
+            # Filter Source B schools by district_id
+            possible_matches = source_b[source_b['district_id'] == district_id_a]
+            
+            # Combine names and old names for matching
+            possible_names = possible_matches['name'].tolist() + possible_matches[['old_name1', 'old_name2', 'old_name3']].stack().tolist()
+            
+            # Apply fuzzy matching on combined names
+            best_match = process.extractOne(velthuis_name, possible_names, scorer=fuzz.token_sort_ratio)
+            
+            all_matches = process.extract(velthuis_name, possible_names, scorer=fuzz.token_sort_ratio)
+            
+            if best_match and best_match[1] >= threshold:
+                best_match_name = best_match[0]
+                
+                # Determine if best match is from current or old names
+                if best_match_name in possible_matches['name'].values:
+                    best_match_row = possible_matches[possible_matches['name'] == best_match_name].iloc[0]
+                else:
+                    old_name_matches = possible_matches[possible_matches[['old_name1', 'old_name2', 'old_name3']].apply(lambda x: best_match_name in x.values, axis=1)]
+                    if not old_name_matches.empty:
+                        best_match_row = old_name_matches.iloc[0]
+                    else:
+                        continue
+                
+                school_id_b = best_match_row['school_id']
+                
+                # Append the match result
+                matches.append({
+                    'school_id_a': school_id_a,
+                    'school_id_b': school_id_b,
+                    'match_score': best_match[1],
+                    'school_name_a': row['school'],
+                    'school_name_b': best_match_row['name'],
+                    'district_id_a': district_id_a,
+                    'district_a': district_a,
+                    'district_b': best_match_row['district'],
+                    'confidence': best_match[1],
+                    'all_matches': all_matches
+                })
+    
+    return pd.DataFrame(matches)
+
+# Run the matching function
+matched_schools = match_schools(source_a, source_b, district_mapping_a, district_mapping_b)
+
+# Save the matching results to a CSV file
+matched_schools.to_csv('school_mapping_results.csv', index=False)
+
+print(f"Total matches found: {len(matched_schools)}")