Skip to content

Instantly share code, notes, and snippets.

Avatar

Akash Raj Patel BinarySpoon

View GitHub Profile
View #10 Titanic Survival Model.py
# A.Importing Libraries -->
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
# Models Used -->
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
View Spam Classifier.py
# LIBRARIES USED -->
import nltk
import string
import pandas as pd
from nltk.corpus import stopwords
# REQUIRED DOWNLOADS -->
nltk.download('punkt')
nltk.download('stopwords')
from sklearn.model_selection import train_test_split
View Ultrasonic Object Detector.c
// Libraries Used -->
#include <LiquidCrystal.h>
#include <HCSR04.h>
// Pins Used -->
#define Trig 6
#define Echo 7
#define MA 7
#define Y_LED 8
View Hypothesis Testing: Effect Of Recession On Housing Prices.py
# libraries used -->
import pandas as pd
import numpy as np
from scipy import stats
import re
# get list of university towns -->
def get_list_of_university_towns():
# Open and read files -->
View Cancer Prediction.py
# libraries used -->
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import precision_score
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
View Date_extractor().py
def date_extractor():
# imports -->
import pandas as pd
import re
from calendar import month_name
import dateutil.parser
from datetime import datetime
# import medical notes -->
doc = []
View Blight ticket prediction model.py
def blight_model():
import pandas as pd
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm.libsvm import predict_proba
# training data
train_data = pd.read_csv('train.csv', encoding = 'ISO-8859-1')