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| import pandas as pd | |
| import pickle | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| with open("static/rf_pipeline.pkl", "rb") as f: | |
| pipeline2 = pickle.load(f) | |
| data = pd.read_csv("static/zone_course_and_colleges.csv") | |
| def get_prediction(college_name, course_name = "Computer Science and Engineering"): | |
| template = pd.DataFrame([[college_name, | |
| course_name, | |
| "OPEN", | |
| "General", | |
| "State", | |
| 2024, | |
| 1]], | |
| columns=['college_name', 'course_name', 'category', 'gender', 'seat_level_attribute', 'year', 'round']) | |
| return pipeline2.predict(template) | |
| def get_colleges(marks, zone, course): | |
| # data = pd.read_csv("static/zone_course_and_colleges.csv") | |
| # print(data[(data["zone"] == zone) & (data["course_name"] == course)]["colleges"].values[0]) | |
| try: | |
| colleges = list(eval(data[(data["zone"] == zone.strip()) & (data["course_name"] == course.strip())]["colleges"].values[0])) | |
| except: | |
| return ["Too less to get into"] | |
| college_data = [] | |
| for college in colleges: | |
| prediciton = get_prediction(college, course) | |
| if prediciton <= marks: | |
| college_data.append(college) | |
| return college_data | |
| def get_courses_for_zone(zone): | |
| row = data[data["zone"] == zone.strip()] | |
| courses = row["course_name"].unique() | |
| return courses |