Mutual-Copy1.py

#!/usr/bin/env python
# coding: utf-8

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import bs4


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import requests


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res=requests.get('http://www.moneycontrol.com/mf/mfinfo/amc_sch_listing.php?ffid=BS')


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type(res)


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soup=bs4.BeautifulSoup(res.text,'lxml')


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soup.select('.boxBg1')


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for i in soup.select('.boxBg1'):
    print(i.text)


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import pandas as pd


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tables = pd.read_html("http://www.moneycontrol.com/mf/mfinfo/amc_sch_listing.php?ffid=BS")


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tables[0]


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datasource2=r'd:\Mutual\MutualFund1.xlsx'


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df=pd.read_excel(datasource2)


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print(df.head())


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df.fillna(0)


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datasource7=r'd:\Mutual\MutualFund2.xlsx'


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df.to_excel(datasource6)


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df3=pd.read_excel(datasource7)


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print(df3.head())


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import numpy as np


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print(df3.loc[:,['LatestNAV']].agg(['mean','median','std']))


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df4=df3.copy()


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df4['Selected']=np.where(df4['LatestNAV']>10.81,1,0)


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df4.head()


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datasource8=r'd:\Mutual\MutualFund3.xlsx'


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df4.to_excel(datasource8)


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import matplotlib.pyplot as plt



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get_ipython().run_line_magic('matplotlib', 'inline')


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from sklearn.model_selection import train_test_split


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X=df4[['LatestNAV']]


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Y=df4[['Selected']]


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X_train,X_test,Y_train,Y_test=train_test_split(X,Y,train_size=0.3)


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X_test


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Y_test


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## Logistic Regression ##
from sklearn.linear_model import LogisticRegression


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model = LogisticRegression()


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model.fit(X_train,Y_train)


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Y_predicted = model.predict(X_test)


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model.predict_proba(X_test)
#this means that , see 0.9878..(the first one) , it says that there is 98% probability that the stock will be selected.


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model.score(X_test,Y_test)


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import matplotlib.pyplot as plt


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get_ipython().run_line_magic('matplotlib', 'inline')


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plt.scatter(df4.LatestNAV,df4.Selected,marker='+',color='red')


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## DONE WITH LOGISTIC REGRESSION ##


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## Let's Start with Decision Tree now 
df5=df4.copy()


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df5.head()


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inputs = df5.drop('Selected',axis='columns')


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target = df5['Selected']


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from sklearn.preprocessing import LabelEncoder
le_scheme = LabelEncoder()
le_nav = LabelEncoder()
le_assets = LabelEncoder()


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inputs['Scheme_n'] = le_company.fit_transform(inputs['Scheme Name'])
inputs['Nav_n'] = le_company.fit_transform(inputs['LatestNAV'])
inputs['Assets_n'] = le_company.fit_transform(inputs['Assets(Rs. cr.)'])


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inputs


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inputs_n = inputs.drop(['Scheme Name','Category','LatestNAV','1 yr Returns (%)','Assets(Rs. cr.)'],axis='columns')


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inputs_n


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target


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from sklearn import tree
model = tree.DecisionTreeClassifier()


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model.fit(inputs_n, target)


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model.score(inputs_n,target)


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model.predict([[12,152,346]])


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datasource8=r'd:\Mutual\MutualFund4.xlsx'


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inputs_n.to_excel(datasource8)


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## Done with decision Tree Now ##


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df4.head()


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df6=df4.loc[df4['Selected'] == 1]


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df6.head()


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datasource9=r'd:\Mutual\MutualFund5.xlsx'


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df6.to_excel(datasource9)


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df7=df6.sort_values('LatestNAV',ascending=False)


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df8=df7.head(5)


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datasource=r'd:\Mutual\MutualFund6.xlsx'


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df8.to_excel(datasource)


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df12=df4.head(50)


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## Linear Regression Model ##
X = df12[['Assets(Rs. cr.)']]


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Y=df12[['LatestNAV']]


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from sklearn.model_selection import train_test_split


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X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3)


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X_train


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Y_train


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from sklearn.linear_model import LinearRegression


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clf = LinearRegression()


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clf.fit(X_test, Y_test)


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clf.predict(X_test)


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clf.score(X_test, Y_test)


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## Done with Linear Regression ##


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## SVM Model ##
from sklearn.model_selection import train_test_split


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df3.info()


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from sklearn.svm import SVR


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import matplotlib.pyplot as matplt


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df9=df7.head(20)


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X=df9[['Assets(Rs. cr.)']]


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Y=df9[['LatestNAV']]


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Y


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X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.4)


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X_train


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Y_train


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svr_lin=SVR(kernel='linear',C=1e3)
svr_poly=SVR(kernel='poly',C=1e3,degree=2)
svr_rbf=SVR(kernel='rbf',C=1e3,gamma=0.1)


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svr_lin.fit(X_train,Y_train)
svr_poly.fit(X_train,Y_train)
svr_rbf.fit(X_train,Y_train)


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matplt.scatter(X_train,Y_train,color='black',label='data')


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matplt.plot(X_train,svr_lin.predict(X_train),color='blue',label='Linear SVR')