ChristopherDaigle

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

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of

ChristopherDaigle

ChristopherDaigle

from lxml import html  
import requests
from time import sleep
import json
import argparse
from collections import OrderedDict
from time import sleep

def parse(ticker):
	url = "http://finance.yahoo.com/quote/%s?p=%s"%(ticker,ticker)

ChristopherDaigle

from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split

X = df_1.drop(['revenue', 'above_ave_rev_yr', 'original_language', 'original_title', 'overview', 'release_date', 'status', 'tagline', 'title'], axis=1)
y = df_1['revenue']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

lm_model = LinearRegression(normalize=True)
r_model = Ridge(normalize=True)

ChristopherDaigle

from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split

X = df_final.drop(['revenue', 'release_date'], axis=1)
y = df_final['revenue']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

lm_model = LinearRegression(normalize=True)
r_model = Ridge(normalize=True)

ChristopherDaigle

from sklearn.impute import KNNImputer

imputer = KNNImputer(n_neighbors=int(round(np.sqrt(df_1.shape[0]))))

df_1.reset_index(inplace = True, drop = True)
impute_df = df_1.drop(columns='release_date')
impute_df_filled = imputer.fit_transform(X=impute_df)

imputed_cols = list(df_1.drop(columns='release_date').columns)
df_impute = pd.DataFrame(data=impute_df_filled, columns=imputed_cols)

ChristopherDaigle

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
numerical = ['orig_title_len', 'overview_len', 'tagline_len', 'title_len',
             'popularity', 'runtime', 'vote_average', 'vote_count']

df_impute_log_minmax = pd.DataFrame(data = df_1_impute_fill).copy()
df_impute_log_minmax[numerical] = scaler.fit_transform(df_1_impute_fill[numerical])

# Preserve final df transformation:
df_impute_transformed = df_impute_log_minmax

ChristopherDaigle

from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import fbeta_score, accuracy_score

models = {'log_model': LogisticRegression(random_state=0),
          'log_cv_model': LogisticRegressionCV(),
          'ab_model':AdaBoostClassifier(random_state=0),
          'rf_model': RandomForestClassifier(random_state=0),

ChristopherDaigle

from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split

X = df_final.drop(['vote_average', 'release_date'], axis=1)
y = df_final['vote_average']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

lm_model = LinearRegression(normalize=True)
r_model = Ridge(normalize=True)