Spaces:
Sleeping
Sleeping
File size: 1,204 Bytes
09d7a91 a60a6e6 09d7a91 a60a6e6 09d7a91 a60a6e6 09d7a91 a60a6e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
from sklearn.preprocessing import MinMaxScaler
from helper import get_scores_optimized
def calculate_final_score(df, job_details):
# print('Inside final score')
columns_to_normalize = ['matched_skills', 'experience', 'education_relevance', 'trait_flag']
df_scored=get_scores_optimized(df,job_details)
scaler = MinMaxScaler()
# print(f'lets see the columns: {df_scored.columns}')
df_scored[columns_to_normalize] = scaler.fit_transform(df_scored[columns_to_normalize])
# print(f'scoring done !!!')
# Define weights
weights = {
'matched_skills': 0.3,
'experience': 0.3,
'education_relevance': 0.3,
'trait_flag': 0.1
}
# Compute final score as a weighted sum
df['final_score'] = (
df_scored['matched_skills'] * weights['matched_skills'] +
df_scored['experience'] * weights['experience'] +
df_scored['education_relevance'] * weights['education_relevance'] +
df_scored['trait_flag'] * weights['trait_flag']
)
df_sorted = df.sort_values(by='final_score', ascending=False)
df_no_full_duplicates = df_sorted.drop_duplicates(keep="first")
return df_no_full_duplicates
|