script stringlengths 113 767k |
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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (
Input,
Conv2D,
MaxPooling2D,
Dropout,
concatenate,
Conv2DTranspose,
UpSampling2D,
)
from tensorflow.keras.optimizers import Adam
from tensorflow.keras... |
# **Code for importing the dataset**
import pandas as pd
Life_Expectancy_Data = pd.read_csv("../input/life-expectancy/Life Expectancy Data.csv")
Life_Expectancy_Data
# **Listing the dataset information which contains number of rows and columns, data types count and memory usage by the dataset.**
Life_Expectancy_Data.... |
# # **Point Couds with zarr**
# Generate Point Clouds while leveraging efficient image loading with zarr.
# Credit: https://www.kaggle.com/code/brettolsen/efficient-image-loading-with-zarr/notebookimport os
import os
import shutil
import time
import PIL.Image as Image
from glob import glob
from tifffile import tifffile... |
import os
from tqdm.auto import tqdm
import time, gc
import numpy as np
import pandas as pd
# pd.set_option('display.max_columns', None)
from matplotlib import pyplot as plt
import cv2
import albumentations as A
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model, Input, load_model
... |
# # Corona Virus 2019
# This Notebook have visualize info for Corona Virus spread all over the word also some insights from the data like which country has maximum spread and which country has max success rate in coronavirus cure.
# # Insight
# Thiland has max(more than 25% people are recoverd) recoverd ratio so the me... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# **Medical Cost Prediction**
# ## *Load the data*
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
insurance = pd.read_csv("../input/insurance/insurance.csv")
insurance.head(5)
# # Explore dataset
insurance.info()
insurance.describe(include="all")
insurance.hist(bins=50, figsize=(12, 8))
plt.sh... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
# # Project Name :- Bike Rental
# ### Project Description -
# Objective of the analysis is to find out the determining factor that drives the demand on bike share rentals,
# construct statistical models and then try to make prediction on rentals based on the information and models we have.
# Exploration and the analysi... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# # Introduction to BDTs
import pandas
import numpy as np
import matplotlib.pyplot as plt
import json
# Load data
hd_Signal = pandas.read_hdf("../input/Signal.h5", "df")
hd_Background = pandas.read_hdf("../input/Background.h5", "df")
hd_Signal.head()
# Select set of variables
variablelist = [
"nJets_OR_T",
"n... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
for dirname, _, filenames in os.walk("/kaggle/input"):
for filename in filenames:
print(os.path.join(dirname, filename))
from sklearn import preprocessing
# Any results you write to the c... |
# #Bellabeat by C
# author: "Chinwe O."
# date: "2023-04-10"
# Bellabeat Case Study in R
# ##About Bellabeat
# Founded in 2013 by Urška Sršen and Sando Mur, Bellabeat is a wellness technology company producing health-focused products targeted at women. With a range of products including the Bellabeat app, the Leaf and ... |
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk("/kaggle/input"):
for filename in filenames:
print(os.path.join(dirname, filename... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # data Visualization
import seaborn as sns # data Visualization
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or p... |
# # PitchFork data wrangling and visualisation
# Table of Contents
# PitchFork data wrangling and visualisation1. Explore data2. Do review scores for an individual artist improve over time, or go down?3. Is the average of score of an artist correlated with the number of reviews?
import sqlite3, datetime
import pandas a... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
df = pd.read_csv("/kaggle/input/nov... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# All data are taken at 250 Hz
# Importing Libraries Requird for the Preprocessing
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import scipy.io
from math import pi
from scipy.fftpack import fft
import scipy.signal as sig
imp... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# # CFG Python Data Challenge - Analyse Sales Data
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
for dirname, _, filenames in os.walk("/kaggle/input"):
for filename in filenames:
print(os.path.join(dirname, filename))
# pd.options.dis... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# <div style="padding:20px;
# color:white;
# margin:10;
# font-size:200%;
# text-align:center;
# display:fill;
# border-radius:5px;
# background-color:#191970;
# overflow:hidden;
# font-weight:700">[S03E12] 🚀 Stacking Tuned Models ⚙️🔧
# # Table of Contents
# - [1. Loading and Inspecting Data](#loading-data)
# - [2. A... |
# # Australian Bushfire - Map analyis
# The Australian bushfire has led to massive loss to wildlife, forest area and has even caused human casualities, inclding firefirghters from U.S. It has even affected the air quality in nearby cities such as sydney and melbourne. We will take a look at fire data obtained from NASA... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import os
import warnings
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import log_loss
warnings.filterwarnings("ignore")
# ## Data Preprocessing
tourney_result = pd.read_csv(
"../input/google-cloud-... |
# ## Exploratory Data Analysis on Corona Virus
# ## What is a Corona Virus?
# As listed on WHO website, Coronaviruses (CoV) are a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SAR... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# ### Content Based Recommendation System
import pandas as pd
df = pd.read_csv("../input/content-based-recomm-sys/movies_metadata.csv")
import os
os.listdir("../input")
df.head()
df["tagline"].fillna("")
df["description"] = df["overview"] + df["tagline"]
# df['description'] = df['description'].fillna('')
df.shape
df.... |
# # Session 3: Exploratory Data Analysis
# First we import relevant libraries.
import pandas as pd
import numpy as np
import matplotlib
# Then we load the file as a data table into the variable `df`.
df = pd.read_csv("../input/salary-dataset/salary_dataset.csv")
# We view the first five records (through `df.head()`) ... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter)... |
# Nesse notebook farei uma análise do conhecido caso do Titanic.
# Será feita uma análise exploratória dos dados, no que seria uma etapa anterior ao processo de predição, para determinação das features (variáveis) mais relevantes na sobrevivência ou não nesta tragédia.
# Importando as bibliotecas necessárias para análi... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("whitegrid")
df_2017 = pd.read_csv("../input/utmb_results_2017.csv")
df_2018 = pd.read_csv("../input/utmb_results_2018.csv")
df_2019 = pd.read_csv("../input/utmb_results_2019.csv")
df_2017.head()
df_2018.head()
d... |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from sklearn.model_selection import (
train_test_split,
KFold,
cross_val_score,
StratifiedKFold,
)
from sklearn.svm import SVC
from sklearn.linear_model... |
# Importing the necessary libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble ... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # data visualization library
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all f... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pycountry
import plotly.express as px
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the in... |
# # Titanic Competition
# This is the notebook of my first all alone, no tutorials, Kaggle competition.
# It will be used for the study of data cleaning, exploratory analysis, data visualization, and machine learning models.
# Feel free to make any comments, suggestions and critics.
import numpy as np
import pandas as ... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# # BERT with KFold
# ## References
# * https://www.kaggle.com/xhlulu/disaster-nlp-keras-bert-using-tfhub
# * https://qiita.com/koshian2/items/81abfc0a75ea99f726b9
# We will use the official tokenization script created by the Google team
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.ker... |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("/kaggle/input/covid19-in-usa/us_counties_covid19_daily.csv")
df
# **Data Cleaning**
# Here we will make Day Date & year seperate Columns for better understanding
df["Date"] = pd.to_datetime(df["date"])
df["Y... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# # NY Rental Properties Pricing
# # Dataset is obtained from kaggle
# https://www.kaggle.com/datasets/ivanchvez/ny-rental-properties-pricing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("/kaggle/input/ny-rental-properties-pricing/NY Realstate Pricing.cs... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
#
# # Table of Contents
# 1. [Introduction](#introduction)
# 1. [Install libraries and packages](#install_libraries_and_packages)
# 1. [Import libraries](#import_libraries)
# 1. [Configure hyper-parameters](#configure_hyper_parameters)
# 1. [Define useful classes](#define_useful_classes)
# 1. [Start the inference proce... |
# ## Loading Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
init_notebook_mode(connected=True)
plt.style.use("ggplot")
from collections import Counter
from wordcloud import WordCloud
from PIL import Image
import urllib.request
import random
from sklearn.preproces... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files und... |
# # GOAL
# ### This notebook gives a summary of a study and explains how regression techniques can be used to predict the price of a house.
# ### The features in the dataset represent different attributes of each house in the dataset.
# ### It is the objective of this project to apply regression models in order to pred... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas_profiling # Profiling is a process that helps us on understanding the data
import plotly.offline # importing plotly in offline mode
import cufflinks as cf # importing cufflinks in offline mode
... |
import pandas as pd
import numpy as np
# # Reading the Movie Rating dataset
# Reading the file:
movie_dataset = pd.read_csv("../input/movie_ratings.csv")
# Dropping user name column:
movie_ratings = movie_dataset.drop("users", axis=1)
print(movie_ratings.head(5))
print(" Shape of movie matrix is :", movie_ratings.shap... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# ### Importing the usual suspects
try:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
plt.style.use("ggplot")
from nltk.corpus import stopword... |
# ## Goal
# In this notebook, we apply the Intelligent search methods like Differential Evolution Algorithm to find the best ML algorithm hyper-parameters.
# Previous options are using either predetermined or randomly generated parameters for the ML algorithms.
# Some of these searching methods are actually a simulatio... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files und... |
# data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
# machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassif... |
import pandas as pd
from json import load
import urllib.request, json
from pandas.io.json import json_normalize
import seaborn as sns
import pylab as plt
cur_url = "https://zenodo.org/api/records/?sort=mostrecent&type=dataset&access_right=open&size=1000"
counter = 0
while True:
print(cur_url)
with urllib.reque... |
import numpy as np
import pandas as pd
import tensorflow as tf
import efficientnet.tfkeras as efn
import glob, os
import pickle
keras = tf.keras
layers = keras.layers
TRAIN_PATH = glob.glob(r"/kaggle/input/cifar10-python/cifar-10-batches-py/data*")
TEST_PATH = [r"/kaggle/input/cifar10-python/cifar-10-batches-py/test_b... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection ... |
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn import preprocessing
import matplotlib.pyplot as plt
dc = pd.read_csv("/kaggle/input/santa-2022/image.csv")
ds = pd.read_csv("/kaggle/input/santa-2022/sample_submission.csv")
print(dc)
print(ds)
dc.head()
ds.head()
dc.shape
ds.shape
dc.dtypes
ds... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
# # Setup
import datetime
from typing import Dict, List
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
np.set_printoptions(precision=3)
pd.set_option("display.precision", 3)
company_df = pd.read_csv("app/resources/Client_Master.csv", dtype=str)
company_df.head()
detail_df = pd.re... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np
import PIL.Image
import glob
import matplotlib.pyplot as plt
import os
from cv2 import resize
import tqdm.auto as tqdm
import cv2
import matplotlib
def format(shape):
x_position = range(0, shape[1], 125)
x_label = [str(x // 5) for x in x_position]
y_position = np.arange(0, shape[0], 125... |
# Analysis of Pokémon Database
# Creator: alopez247
# Notebook Author: João Paulo Ribeiro dos Santos (joaopauloribsantos)
# This notebook is intended to apply some concepts and methods that I am learning. For this reason, that it will be updated frequently, until I can answer two questions that have always puzzled me a... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option("display.float_format", lambda x: "%.3f" % x)
pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_columns", 1000)
plt.rcParams["figure.figsize"] = (11, 5)
train_df = pd.read_csv("/kaggle/input/tita... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import scipy
from scipy import stats
from scipy.stats import norm, skew
import warnings
def ignore_warn(*args, **kwargs):
pass... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# # Introduction
# This notebook is a development of the final exercise from the ‘[Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning)’ micro-course.
#
import math
from operator import itemgetter
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn.base... |
# # Introduction
# The purpose of this kernel is to build a predictive model in domain of anomaly detection using Tennessee Eastman Process Simulation Dataset.
# I will use some statistical and neural network approaches.
import numpy as np
import pandas as pd
import pyreadr
# # Data preparation
# The original dataset ... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
import warnings
warnings.filterwarnings("ignore")
# Utils
from os import path
# Data
import numpy as np
import pandas as pd
# Viz
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
sns.set(style="darkgr... |
sns.set_style("darkgrid")
pd.set_option("display.max_columns", 500)
train = pd.read_csv(
"/kaggle/input/house-prices-advanced-regression-techniques/train.csv"
)
test = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/test.csv")
train.head()
train.info()
train.describe()
# # Checking # of NA
# ... |
# # Explanation
# > This data includes 9 Column
# > But variable values not enough. For example ther's not Player Shoot Power, PointCount,AssistCount etc... Therefore some analiysing will be short.
# 1. We will firstly study the columns and data
# 1. Something analysing with using pandas libraray and we will know panda... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
from matplotlib import pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import PIL.Image as Image, PIL.ImageDraw as ImageDraw, PIL.ImageFont as ImageFont
import random
import os
import cv2
import gc
from tqdm.auto import tqdm
import numpy as np
i... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory... |
# # Импорты
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Считаем данные и подготовим их к дальнейшему анализу
df = pd.read_csv("../input/lending-club/accepted_2007_to_2018Q4.csv.gz")
# df.head()
with pd.option_context("display.max_columns", None):
display(df.head()... |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, file... |
# ## Current & Forecast weather data collection
# ### 1. Overview
# In this notebook, I am exploring how to summarize and visualize weather forecast information in a way that's easy and useful for people. For example, given all the weather data we could retrieve, which ones are the most relevant to people's everyday li... |
# # Linear regression (predicting a continuous value):
# *** Question:**
# > Weather in Szeged 2006-2016: Is there a relationship between humidity and temperature? What about between humidity and apparent temperature? Can you predict the apparent temperature given the humidity?
import numpy as np # linear algebra
... |
# **Objective**
# The objective of milestone-1 is to perform data preprocessing and EDA to understand customer churn. The output of milestone-1 will be used in milestones-2 and 3 to build machine learning models to predict customer churn and to create an interactive dashboard for decision making. In order to do that, f... |
# ###### Notebook created by: Arnav Chavan (@[carnav0400](https://www.kaggle.com/carnav0400)), Udbhav Bamba (@[ubamba98](https://www.kaggle.com/ubamba98))
# ## NOTE: Turn on the Internet and GPU for this kernal before starting
# # How to add dataset to the kernal
# * Click on "Add Data"
# * Search "CLabsCVcomp"
# * Cli... |
#
# Note: This is a work in progress notebook!
# - Lot can be done in the feature engineering.
# - Also passing Spark dataframe to Tensorflow without converting it to pandas.
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from pyspark.sql import SparkSession
from pyspark.sql import functions as ... |
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