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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 |