import pandas

url = 'https://raw.githubusercontent.com/deepanshumehtaa/csvfiles/master/xyz.csv'
df = pd.read_csv(url, index_col=0, delimiter=',')
df = df.reset_index()
df.head(5)

csvfiles

use this to access csv file for inbuilt datasets lik iris to load them use

For Seaborn Dataset

iris = sns.load_dataset('iris')

-OR-

from sklearn.datasets import load_iris
iris = load_iris()
data = iris.data
column_names = iris.feature_names

For Pandas

import pandas as pd
df = pd.DataFrame(iris.data, iris.feature_names)

For Sklearn

befor you start with sea born database https://medium.com/@haydar_ai/learning-data-science-day-9-linear-regression-on-boston-housing-dataset-cd62a80775ef

from sklearn.datasets import load_iris
sk_data = load_iris()
print(sk_data)

df = pd.DataFrame(sk_data.data, columns=sk_data.feature_names)
df.head()

Iris is decleared in Sklearn so to convert it into df

For all the Datasets present for ML

import seaborn as sb
df = sns.load_dataset('iris')
print( sns.get_dataset_names() )

To split the data without train_test_split

# splitting the data BUT, not randomly
X =df.drop(['car'], axis=1)
y =df.car

train_index = int(0.8 * len(X))
X_train, X_test = X[:train_index], X[train_index:]
y_train, y_test = y[:train_index], y[train_index:]

. Follow this Documentation for Keras Dataset: https://jovianlin.io/datasets-within-keras/

url = 'https://raw.githubusercontent.com/deepanshumehtaa/csvfiles/master/xyz.csv'
df = pd.read_csv(url, index_col=0, delimiter=',')
df = df.reset_index()
df.head(5)

use this to access csv file for inbuilt datasets lik iris to load them use

For Seaborn Dataset

iris = sns.load_dataset('iris')

-OR-

from sklearn.datasets import load_iris
iris = load_iris()
data = iris.data
column_names = iris.feature_names

For Pandas

import pandas as pd
df = pd.DataFrame(iris.data, iris.feature_names)

For Sklearn

befor you start with sea born database https://medium.com/@haydar_ai/learning-data-science-day-9-linear-regression-on-boston-housing-dataset-cd62a80775ef

from sklearn.datasets import load_iris
sk_data = load_iris()
print(sk_data)

df = pd.DataFrame(sk_data.data, columns=sk_data.feature_names)
df.head()

Iris is decleared in Sklearn so to convert it into df

For all the Datasets present for ML

import seaborn as sb
df = sns.load_dataset('iris')
print( sns.get_dataset_names() )

To split the data without train_test_split

# splitting the data BUT, not randomly
X =df.drop(['car'], axis=1)
y =df.car

train_index = int(0.8 * len(X))
X_train, X_test = X[:train_index], X[train_index:]
y_train, y_test = y[:train_index], y[train_index:]

. Follow this Documentation for Keras Dataset: https://jovianlin.io/datasets-within-keras

From G-Drive

import pandas as pd
import requests
from io import StringIO

url = requests.get('https://doc-0g-78docs.googleusercontent.com/docs/securesc/token')
csv_raw = StringIO(url.text)
df = pd.read_csv(csv_raw)

Download Dataset from Kaggle using kaggle API

""" !pip install -q kaggle: installing this packageing but quitely with no loading bars

!mkdir -p ~/.kaggle

the command is creating a -p as parent directory if not exist at root(~/)

!cp kaggle.json ~/.kaggle/

copy the api secrete key to main kaggle folder Ensure kaggle.json is in the location ~/.kaggle/kaggle.json to use the API.

!ls ~/.kaggle if present return nothing else error

!chmod 600 /root/.kaggle/kaggle.json -- 600 permissions means that only the owner of the file has full read and write access to it. Once a file permission is set to 600, no one else can access the file.

calling kaggle api with kaggle's python package: !kaggle datasets download -d emmarex/plantdisease """ !pip install -q kaggle !mkdir -p ~/.kaggle !cp <your_key_file>.json ~/.kaggle/ !ls ~/.kaggle !mv '/root/.kaggle/<your_key_file>.json' '/root/.kaggle/kaggle.json' !chmod 600 /root/.kaggle/kaggle.json # set permission

Run the Download API

!kaggle datasets download -d devashish0507/big-mart-sales-prediction

Unzip that also

!unzip plantdisease.zip