# Importing essential libraries
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
# Loading the dataset
df = pd.read_csv('admission_predict.csv')
# Returns number of rows and columns of the dataset
df.shape
# Returns the first x number of rows when head(num). Without a number it returns 5
df.head()
# Returns the first x number of rows when tail(num). Without a number it returns 5
df.tail()
# Returns an object with all of the column headers
df.columns
# Returns basic information on all columns
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 9 columns):
Serial No. 500 non-null int64
GRE Score 500 non-null int64
TOEFL Score 500 non-null int64
University Rating 500 non-null int64
SOP 500 non-null float64
LOR 500 non-null float64
CGPA 500 non-null float64
Research 500 non-null int64
Chance of Admit 500 non-null float64
dtypes: float64(4), int64(5)
memory usage: 35.3 KB
# Returns basic statistics on numeric columns
df.describe().T
# Returns different datatypes for each columns (float, int, string, bool, etc.)
df.dtypes
# Returns true for a column having null values, else false
df.isnull().any()
# Renaming the columns with appropriate names
df = df.rename(columns={'GRE Score': 'GRE', 'TOEFL Score': 'TOEFL', 'LOR ': 'LOR', 'Chance of Admit ': 'Probability'})
df.head()
# Visualizing the feature GRE
fig = plt.hist(df['GRE'], rwidth=0.7)
plt.title("Distribution of GRE Scores")
plt.xlabel('GRE Scores')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['TOEFL'], rwidth=0.7)
plt.title('Distribution of TOEFL Scores')
plt.xlabel('TOEFL Scores')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['University Rating'], rwidth=0.7)
plt.title('Distribution of University Rating')
plt.xlabel('University Rating')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['SOP'], rwidth=0.7)
plt.title('Distribution of SOP')
plt.xlabel('SOP Rating')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['LOR'], rwidth=0.7)
plt.title('Distribution of LOR Rating')
plt.xlabel('LOR Rating')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['CGPA'], rwidth=0.7)
plt.title('Distribution of CGPA')
plt.xlabel('CGPA')
plt.ylabel('Count')
plt.show()
# Visualizing the feature TOEFL
fig = plt.hist(df['Research'], rwidth=0.7)
plt.title('Distribution of Research Papers')
plt.xlabel('Research')
plt.ylabel('Count')
plt.show()
# Removing the serial no, column
df.drop('Serial No.', axis='columns', inplace=True)
df.head()
# Replacing the 0 values from ['GRE','TOEFL','University Rating','SOP','LOR','CGPA'] by NaN
df_copy = df.copy(deep=True)
df_copy[['GRE','TOEFL','University Rating','SOP','LOR','CGPA']] = df_copy[['GRE','TOEFL','University Rating','SOP','LOR','CGPA']].replace(0, np.NaN)
df_copy.isnull().sum()
# Splitting the dataset in features and label
X = df_copy.drop('Probability', axis='columns')
y = df_copy['Probability']
# Using GridSearchCV to find the best algorithm for this problem
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
# Creating a function to calculate best model for this problem
def find_best_model(X, y):
models = {
'linear_regression': {
'model': LinearRegression(),
'parameters': {
'normalize': [True,False]
}
},
'lasso': {
'model': Lasso(),
'parameters': {
'alpha': [1,2],
'selection': ['random', 'cyclic']
}
},
'svr': {
'model': SVR(),
'parameters': {
'gamma': ['auto','scale']
}
},
'decision_tree': {
'model': DecisionTreeRegressor(),
'parameters': {
'criterion': ['mse', 'friedman_mse'],
'splitter': ['best', 'random']
}
},
'random_forest': {
'model': RandomForestRegressor(criterion='mse'),
'parameters': {
'n_estimators': [5,10,15,20]
}
},
'knn': {
'model': KNeighborsRegressor(algorithm='auto'),
'parameters': {
'n_neighbors': [2,5,10,20]
}
}
}
scores = []
for model_name, model_params in models.items():
gs = GridSearchCV(model_params['model'], model_params['parameters'], cv=5, return_train_score=False)
gs.fit(X, y)
scores.append({
'model': model_name,
'best_parameters': gs.best_params_,
'score': gs.best_score_
})
return pd.DataFrame(scores, columns=['model','best_parameters','score'])
find_best_model(X, y)
# Using cross_val_score for gaining highest accuracy
from sklearn.model_selection import cross_val_score
scores = cross_val_score(LinearRegression(normalize=True), X, y, cv=5)
print('Highest Accuracy : {}%'.format(round(sum(scores)*100/len(scores)), 3))
Highest Accuracy : 81.0%
# Splitting the dataset into train and test samples
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=5)
print(len(X_train), len(X_test))
400 100
# Creating Linear Regression Model
model = LinearRegression(normalize=True)
model.fit(X_train, y_train)
model.score(X_test, y_test)
# Prediction 1
# Input in the form : GRE, TOEFL, University Rating, SOP, LOR, CGPA, Research
print('Chance of getting into UCLA is {}%'.format(round(model.predict([[337, 118, 4, 4.5, 4.5, 9.65, 0]])[0]*100, 3)))
Chance of getting into UCLA is 92.855%
# Prediction 2
# Input in the form : GRE, TOEFL, University Rating, SOP, LOR, CGPA, Research
print('Chance of getting into UCLA is {}%'.format(round(model.predict([[320, 113, 2, 2.0, 2.5, 8.64, 1]])[0]*100, 3)))
Chance of getting into UCLA is 73.627%