markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Create model | def create_model(input_shape: Tuple[int], output_shape: int,
activation, loss, meta_shape: Optional[int] = None,
task: str = "B", learning_rate: float = 0.001,
pretrain: bool = False) -> models.Model:
"""
The function for creating model.
Parameters
---... | _____no_output_____ | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
Training model | def train(path_list: np.ndarray, target: np.ndarray, loss,
meta_data: Optional[np.ndarray] = None, task: str = "B"):
"""
The function for training model.
Parameters
----------
path_list : np.ndarray
The path list of all image data.
target : np.ndarray
The array of targ... | _____no_output_____ | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
Training models TaskA | meta_features =\
asset_df_A['collection.name'].unique().tolist() + ['num_sales']
path_list = asset_df_A['full_path'].values
meta_data = asset_df_A[meta_features].values
target = asset_df_A['target'].values
model_A = train(path_list, target, losses.mean_squared_error, meta_data,
task="A")
# save_mo... | 2021-11-14 08:31:09.150668: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-11-14 08:31:09.155139: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful N... | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
TaskA（画像のみ） | path_list = asset_df_A['full_path'].values
target = asset_df_A['target'].values
model_A = train(path_list, target, losses.mean_squared_error,
task="B") | starting training
*------------------------------*
Epoch 1/100
1224/1224 [==============================] - 217s 175ms/step - loss: 2.7349 - mae: 1.0369 - mse: 2.7349 - val_loss: 2.2998 - val_mae: 0.9054 - val_mse: 2.2998
Epoch 2/100
1224/1224 [==============================] - 211s 172ms/step - loss: 1.7788 - mae: 0.8... | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
TaskB | path_list = asset_df_B['full_path'].values
target = asset_df_B['target'].values
model_B = train(path_list, target, losses.mean_squared_error)
# save_model(model_B, "../models/baselineB.pkl") | starting training
*------------------------------*
Epoch 1/100
293/293 [==============================] - 57s 181ms/step - loss: 0.5716 - mae: 0.5127 - mse: 0.5716 - val_loss: 0.3407 - val_mae: 0.3353 - val_mse: 0.3407
Epoch 2/100
293/293 [==============================] - 52s 176ms/step - loss: 0.4381 - mae: 0.4247 - ... | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
Evaluate model Task A | file_name = "../models/baselineA.pkl"
model = load_model(file_name)
meta_features =\
asset_df_A['collection.name'].unique().tolist() + ['num_sales']
path_list = np.vstack(
(asset_df_A['full_path'].values.reshape(-1, 1),
asset_df_B['full_path'].values.reshape(-1, 1))
).reshape(-1)
meta_data = np.vstack(
... | _____no_output_____ | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
Task B | file_name = "../models/baselineB.pkl"
model = load_model(file_name)
path_list = asset_df_B['full_path'].values
meta_data = asset_df_B[meta_features].values
target = asset_df_B['target'].values
train_path, val_path, train_meta, val_meta, train_y, val_y =\
train_test_split(path_list, meta_data, target, test_size=0.... | _____no_output_____ | MIT | notebooks/training_model.ipynb | nft-appraiser/nft-appraiser-ml |
Being asked to leave others or groupsBeing restricted from contact with othersDistancing self from relationshipsIsolationLack of meaningful social groupLonelinessNot being understoodphysiological barriers | pos_txt_files[0:5]
pos_files1 = []
for pos_file in pos_files:
pos_file = pos_file.split('\\')
pos_file = pos_file[1].split('.knowtator')
pos_file = pos_file[0]
pos_files1.append(pos_file)
pos_files1
neg_files1 = []
for neg_file in neg_files:
neg_file = neg_file.split('\\')
neg_file = neg_file[1]... | _____no_output_____ | Apache-2.0 | eHostXML_ext.ipynb | phzpan/6950_nlp |
_*Using Qiskit Aqua algorithms, a how to guide*_This notebook demonstrates how to use the `Qiskit Aqua` library to invoke an algorithm and process the result.Further information may be found for the algorithms in the online [Aqua documentation](https://qiskit.org/documentation/aqua/algorithms.html).Algorithms in Aqua ... | from qiskit.aqua import Operator | _____no_output_____ | Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
As input, for an energy problem, we need a Hamiltonian and so we first create a suitable `Operator ` instance. In this case we have a paulis list, as below, from a previously computed Hamiltonian, that we saved, so as to focus this notebook on using the algorithms. We simply load these paulis to create the original Ope... | pauli_dict = {
'paulis': [{"coeff": {"imag": 0.0, "real": -1.052373245772859}, "label": "II"},
{"coeff": {"imag": 0.0, "real": 0.39793742484318045}, "label": "ZI"},
{"coeff": {"imag": 0.0, "real": -0.39793742484318045}, "label": "IZ"},
{"coeff": {"imag": 0.0, "real": -0.011... | _____no_output_____ | Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
Let's start with a classical algorithmWe can now use the Operator without regard to how it was created. We chose to start this tutorial with a classical algorithm as it involves a little less setting up than the `VQE` quantum algorithm we will use later. Here we will use `ExactEigensolver` to compute the minimum eigen... | from qiskit.aqua.algorithms import ExactEigensolver
ee = ExactEigensolver(qubit_op)
result = ee.run()
print(result['energy']) | -1.857275030202378
| Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
Now let's show the `declarative` approach. Here we need to prepare a configuration dictionary of parameters to define the algorithm. Again we we will use the ExactEigensolver and need to create an `algorithm` where it is named by `name`. The name comes from a `CONFIGURATION` dictionary in the algorithm and this name ... | from qiskit.aqua import run_algorithm
from qiskit.aqua.input import EnergyInput
aqua_cfg_dict = {
'algorithm': {
'name': 'ExactEigensolver'
}
}
algo_input = EnergyInput(qubit_op)
result = run_algorithm(aqua_cfg_dict, algo_input)
print(result['energy']) | -1.8572750302023808
| Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
Lets switch now to using a Quantum algorithm.We will use the Variational Quantum Eigensolver (VQE) to solve the same problem as above. As its name implies its uses a variational approach. An ansatz (a variational form) is supplied and using a quantum/classical hybrid technique the energy resulting from evaluating the ... | aqua_cfg_dict = {
'algorithm': {
'name': 'VQE',
'operator_mode': 'matrix'
},
'variational_form': {
'name': 'RYRZ',
'depth': 3,
'entanglement': 'linear'
},
'optimizer': {
'name': 'L_BFGS_B',
'maxfun': 1000
},
'backend': {
'name':... | -1.8572750302012253
| Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
And now `programmatic` Here we create the variational form and optimizer and then pass them to VQE along with the Operator. The backend is created and passed to the algorithm so it can be run there. | from qiskit import BasicAer
from qiskit.aqua.algorithms import VQE
from qiskit.aqua.components.variational_forms import RYRZ
from qiskit.aqua.components.optimizers import L_BFGS_B
var_form = RYRZ(qubit_op.num_qubits, depth=3, entanglement='linear')
optimizer = L_BFGS_B(maxfun=1000)
vqe = VQE(qubit_op, var_form, optimi... | -1.8572750301886618
| Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
While a backend can be passed directly to the quantum algorithm run(), internally it will be detected as such and wrapped as a QuantumInstance. However by doing this explicitly yourself, as below, various parameters governing the execution can be set, including in more advanced cases ability to set noise models, coupli... | from qiskit.aqua import QuantumInstance
from qiskit.transpiler import PassManager
var_form = RYRZ(qubit_op.num_qubits, depth=3, entanglement='linear')
optimizer = L_BFGS_B(maxfun=1000)
vqe = VQE(qubit_op, var_form, optimizer)
backend = BasicAer.get_backend('statevector_simulator')
qi = QuantumInstance(backend=backend,... | -1.8572750302012366
| Apache-2.0 | aqua/algorithm_introduction_with_vqe.ipynb | renier/qiskit-tutorials-community |
Notebook 2: Requesting information After getting all the access token as well as refreshing the token, we started requesting information for our analysis. Just to remind, our four goals are to find out which are the top twenty friends that like our post the most, demographic for places we have been tagged, reactions f... | import requests
import importlib
import json
import pandas as pd
import keys_project
importlib.reload(keys_project)
keychain = keys_project.keychain
d={}
d['access_token']=keychain['facebook']['access_token'] # Getting the long-lived access token | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
Below are all of the helper functions that we have used. The return type of a response from the graph api is not easy to parse and hence we convert all repsonses to JSON. The other functions are supplementing our data requests and modifications as described in the program level docs. | def response_to_json(response):
'''
This function converts the response into json format
Parameter:
response: the request response to convert to json
Return:
the response in json
'''
string_response = response.content.decode('utf-8') #decoding the response to string
re... | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
Last but not least, we imported the dictionary into csv file for later analysis in Notebook 3. This question took us quite long time. However, the questions later on were pretty straightforward and similar to this question. Question: Getting the number of facebook reactions of each reaction type for a particular uploa... | def reaction_statistics(id_,limit,fb_upload_type):
'''
This function gets the total reactions of each feed
ParameterL
id_: a string id to a facebook object such as a page or person
limit: the limit to the numner of posts obtained from the request in string
fb_upload_type: a valid t... | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
Hence, for each cell we can see the upload_type ID to identify the post or photo and the number of reactions for each upload. QUESTION: Obtaining feed data to anaylize the kinds, times and popularity of a user or page's feed. In this question, we get feed information for the artist Bon Dylan (though are function us a... | def feed_data(object_id,limit):
'''
This function generates a list of dictionaries for each feed of information
Parameters:
object_id: the id of the object posting events in string
limit: the number of most recent events in string
Return:
a list of dictionaries where each data i... | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
Question: Get the top twenty frequency of friends who like our post In the cell below, it is our code for the first question, which is the top friends who like our post the most. First, we created a function to convert the response into json format since we would be making a lot of requests and create dictionary from ... | def friend_likes(id_,limit,fb_upload_type):
'''
This function gets a dictionary for each kind of reactions for each post
Parameter:
id_: a string id to a facebook object such as a page or person
limit: the limit to the numner of posts obtained from the request in string
fb_upload_ty... | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
Question: Demographic analysis for place that we have been tagged In this question, we want to explore the places that we have travelled and been tagged on Facebook. We want to create a demographic plot that show where we have been based on the latitudes and longitudes. Since we already know how to perform a GET reque... | def tagged_data(object_id):
'''
This function generates a dictionary which includes the longitudes, latitudes, and names for places.
Parameter:
id_: a string id to a facebook object such as a page or person
Return:
a list of dictionaries of latitude,longitude, country and name of tagged... | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
We will import a dataframe that contains the data about latitude, longitude, and name. Then, we created a csv file out of this dataframe. | df_tagged_places= pd.DataFrame(tagged_data('me'))
to_csv('df_tagged_places.csv',df_tagged_places) | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
We then showed the first ten row in this dataframe. | df_tagged_places.head(10) | _____no_output_____ | MIT | Notebook_2/Notebook_2.ipynb | nguyenst1/facebook-api-analysis |
[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb) Tra... | !wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash
import nlu | --2021-05-05 05:38:30-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... ... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
2. Download IMDB datasethttps://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviewsIMDB dataset having 50K movie reviews for natural language processing or Text analytics.This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a... | ! wget http://ckl-it.de/wp-content/uploads/2021/01/IMDB-Dataset.csv
import pandas as pd
train_path = '/content/IMDB-Dataset.csv'
train_df = pd.read_csv(train_path)
# the text data to use for classification should be in a column named 'text'
# the label column must have name 'y' name be of type str
columns=['text','y'... | _____no_output_____ | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
3. Train Deep Learning Classifier using nlu.load('train.sentiment')You dataset label column should be named 'y' and the feature column with text data should be named 'text' | import nlu
from sklearn.metrics import classification_report
# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns
# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation
trainable_pipe = nlu.load('train.sentiment')
fitted... | tfhub_use download started this may take some time.
Approximate size to download 923.7 MB
[OK!]
sentence_detector_dl download started this may take some time.
Approximate size to download 354.6 KB
[OK!]
precision recall f1-score support
negative 0.82 0.88 0.85 26
neu... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
4. Test the fitted pipe on new example | fitted_pipe.predict('It was one of the best films i have ever watched in my entire life !!') | _____no_output_____ | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
5. Configure pipe training parameters | trainable_pipe.print_info() | The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :
>>> pipe['sentiment_dl'] has settable params:
pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1
pipe['sentiment_dl'].setLr(0.005) | I... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
6. Retrain with new parameters | # Train longer!
trainable_pipe['sentiment_dl'].setMaxEpochs(5)
fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])
# predict with the trainable pipeline on dataset and get predictions
preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')
#sentence detector that is part of the pipe generates sone N... | precision recall f1-score support
negative 0.92 0.92 0.92 26
neutral 0.00 0.00 0.00 0
positive 1.00 0.75 0.86 24
accuracy 0.84 50
macro avg 0.64 0.56 0.59 ... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
7. Try training with different Embeddings | # We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!
nlu.print_components(action='embed_sentence')
trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')
# We need to train longer and user smaller LR for NON-USE base... | sent_small_bert_L12_768 download started this may take some time.
Approximate size to download 392.9 MB
[OK!]
sentence_detector_dl download started this may take some time.
Approximate size to download 354.6 KB
[OK!]
precision recall f1-score support
negative 0.87 0.77 0.82 ... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
7.1 evaluate on Test Data | preds = fitted_pipe.predict(test_df,output_level='document')
#sentence detector that is part of the pipe generates sone NaNs. lets drop them first
preds.dropna(inplace=True)
print(classification_report(preds['y'], preds['trained_sentiment'])) | precision recall f1-score support
negative 0.85 0.75 0.80 246
neutral 0.00 0.00 0.00 0
positive 0.84 0.81 0.83 254
accuracy 0.78 500
macro avg 0.56 0.52 0.54 ... | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
8. Lets save the model | stored_model_path = './models/classifier_dl_trained'
fitted_pipe.save(stored_model_path) | Stored model in ./models/classifier_dl_trained
| Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
9. Lets load the model from HDD.This makes Offlien NLU usage possible! You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk. | hdd_pipe = nlu.load(path=stored_model_path)
preds = hdd_pipe.predict('It was one of the best films i have ever watched in my entire life !!')
preds
hdd_pipe.print_info()
| _____no_output_____ | Apache-2.0 | nlu/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb | fcivardi/spark-nlp-workshop |
Image filtering-Convolution | import numpy as np
import cv2
import matplotlib.pyplot as plt
# loading an orange image
imageBGR = cv2.imread('orange.jpg',-1)
# convert the image from BGR color space to RGB
imageRGB=cv2.cvtColor(imageBGR, cv2.COLOR_BGR2RGB)
plt.imshow(imageRGB)
imageRGB.shape | _____no_output_____ | MIT | OpenCV_Image Filtering.ipynb | deepnetworks555/openCV-jupyter |
Averaging | kernel = np.ones((10,10),np.float32)/100
result = cv2.filter2D(imageRGB,-1,kernel)
plt.subplot(121),plt.imshow(imageRGB),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(result),plt.title('Averaging')
plt.xticks([]), plt.yticks([])
plt.show() | _____no_output_____ | MIT | OpenCV_Image Filtering.ipynb | deepnetworks555/openCV-jupyter |
Gaussian Blur | blured_image = cv2.GaussianBlur(imageRGB,(21,21),10)
plt.figure(figsize=(10,10))
plt.subplot(121),plt.imshow(imageRGB),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(blured_image),plt.title('blured_image')
plt.xticks([]), plt.yticks([])
plt.show() | _____no_output_____ | MIT | OpenCV_Image Filtering.ipynb | deepnetworks555/openCV-jupyter |
Assignment 7 Chapter 6 Student ID: *Double click here to fill the Student ID* Name: *Double click here to fill the name* 1We perform best subset, forward stepwise, and backward stepwise selection on a single data set. For each approach, we obtain $p + 1$ models, containing $0, 1, 2, . . . ,p$ predictors. Explain you... | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (b) Consider $(6.13)$ with $p=1$. For some choice of $y_1$ and $\lambda>0$, plot $(6.13)$ as a function of $\beta_1$. Your plot should confirm that $(6.13)$ is solved by $(6.15)$. > Ans: *double click here to answer the question.* | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
9In this exercise, we will predict the number of applications received using the other variables in the `College` data set. (a) Split the data set into a training set and a test set. Use `train_test_split()` function. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
(b) Fit a **linear** model using least squares on the training set, and report the test error obtained. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (c) Fit a **ridge** regression model on the training set, with $λ$ chosen by cross-validation. Report the test error obtained. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (d) Fit a **lasso** model on the training set, with $λ$ chosen by cross-validation. Report the test error obtained, along with the number of non-zero coefficient estimates. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (e) Fit a **PCR** model on the training set, with *M* chosen by crossvalidation. Report the test error obtained, along with the value of *M* selected by cross-validation. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (f) Fit a **PLS** model on the training set, with *M* chosen by cross-validation. Report the test error obtained, along with the value of *M* selected by cross-validation. | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* (g) Comment on the results obtained. How accurately can we predict the number of college applications received? Is there much difference among the test errors resulting from these five approaches? | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
> Ans: *double click here to answer the question.* 11We will now try to predict per capita crime rate in the `Boston` data set. (a) Try out some of the regression methods explored in this chapter, such as **best subset** selection, the **lasso**, **ridge** regression, and **PCR**. Present and discuss results for the a... | # coding your answer here. | _____no_output_____ | MIT | static_files/assignments/Assignment7.ipynb | phonchi/nsysu-math524 |
IntegersPython represents integers (positive and negative whole numbers) using the`int` (immutable) type. For immutable objects, there is no difference betweena variable and an object di erenc | (58).bit_length()
str='11'
d=int(str)
d
b=int(str,2)
b
divmod(23,5)
round(100.89,2)
round(100.89,-2)
round(100.8936,3)
(4.50).as_integer_ratio() | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
The `fractions` ModulePython has the fraction module to deal with parts of a fraction. | import fractions
dir(fractions)
help(fractions.Fraction)
from fractions import Fraction
def rounding_float(number,place):
return round(number,place)
rounding_float(120.6765545362663,5)
def float_to_fractions(number):
return Fraction(*number.as_integer_ratio())
float_to_fractions(12.5)
def get_denominator(num1,n... | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
The `decimal` ModuleWhen we need exact decimal foating-point numbers, Python has an additional immutable float type, the decimal.Decimal. | import decimal
# dir(decimal)
help(decimal.Decimal)
sum(0.1 for i in range(10))==1.0
from decimal import Decimal
sum(Decimal('0.1') for i in range(10))==1.0 | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
While The `math` and `cmath` modules are not suitable for the decimalmodule, its built-in functions such as `decimal.Decimal.exp(x)` are enoughto most of the problems. Other Representations | bin(120)
hex(123)
oct(345) | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
Functions to Convert Between Different BasesConverts a number in any base smaller than 10 to the decimal base: | def convert_to_decimal(number, base):
multiplier, result = 1, 0
while number > 0:
result += number%10*multiplier
multiplier *= base
number = number//10
return result
def test_convert_to_decimal():
number, base = 1001, 2
assert(convert_to_decimal(number, base) == 9)
print(... | Tests passed!
| MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
Convert a number from a decimal base to anyother base (up to 20) | def convert_from_decimal_larger_bases(number, base):
strings = "0123456789ABCDEFGHIJ"
result = ""
while number > 0:
digit = number%base
result = strings[digit] + result
number = number//base
return result
def test_convert_from_decimal_larger_bases():
number, base = 31, 16
... | Tests in this module have passed!
| MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
Greatest Common DivisorThe greatest common divisor (gcd) betweentwo given integers: | def finding_gcd(a, b):
''' implements the greatest common divider algorithm '''
while(b != 0):
result = b
a, b = b, a % b
return result
finding_gcd(2,5)
finding_gcd(3,6) | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
The `Random` Module | import random
help(random)
my_list=[2,5,6,7,8,9]
random.choice(my_list)
random.sample(my_list,2)
random.shuffle(my_list)
my_list
random.randint(1,10) | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
Fibonacci SequencesTo find the nth number in a Fibonacci sequence in three ways: (a) with a recursive O(2n) runtime; (b) with a iterative O(n2) runtime; and (c) using a formula that gives a O(1) runtime but is not precise after around the 70th element: | def find_fibonacci_seq_rec(n):
if n < 2:
return n
return find_fibonacci_seq_rec(n-1) + find_fibonacci_seq_rec(n-2)
find_fibonacci_seq_rec(8)
def find_fibonacci_seq_iter(n):
if n < 2:
return n
a, b = 0, 1
for i in range(n):
a, b = b, a + b
return a
find_fibonacci_seq_iter... | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
PrimesThe following program finds whether a number is a prime in three ways:(a) brute force; (b) rejecting all the candidates up to the square root of thenumber; and (c) using the Fermat's theorem with probabilistic tests: | import math
import random
def finding_prime(number):
num = abs(number)
if num < 4 :
return True
for x in range(2, num):
if num % x == 0:
return False
return True
finding_prime(5)
finding_prime(4)
def finding_prime_sqrt(number):
num = abs(number)
if num < 4 :
... | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
The `math` module | import math
help(math)
dir(math)
math.__spec__ | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
Number-theoretic and representation functions | for i in dir(math):
if i[0] !='_':
print(i,end="\t")
print(len(dir(math)))
num1=6
num2= -56
num3=45.9086
num4= -45.898
math.ceil(num3)
math.ceil(num4)
math.floor(num3)
math.floor(num4)
math.copysign(num1,num2)
math.fabs(num2)
math.factorial(5)
num=9
math.isnan(num) | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
The `NumPy` ModuleThe NumPy module provides array sequences that can store numbers orcharacters in a space-efficient way. Arrays in NumPy can have any ar-bitrary dimension. They can be generated from a list or a tuple with thearray-method, which transforms sequences of sequences into two dimensionalarrays: | import numpy as np
x = np.array( ((11,12,13), (21,22,23), (31,32,33)) )
x
x.ndim | _____no_output_____ | MIT | basic/Numbers.ipynb | sanikamal/awesome-python-examples |
基于机器学习数据库飞速上线AI应用大家平时可能都会打车，从出发的地点到目的地，行程耗时可能会存在多种因素，比如天气，是否周五，如果获取更准确的耗时预测，对人来说是一个复杂的问题，而对机器就变得很简单，今天的任务就是开发一个通过机器学习模型进行出租车行程耗时的实时智能应用，整个应用开发是基于[notebook](http://ipython.org/notebook.html)![出租车行程耗时预测](https://th.bing.com/th/id/Rcf52e9678006c3e99a98cf88a216e38d?rik=oQN4iVqyXXjYNg&riu=http%3a%2f%2fi1.hexun.com%2f2020-0... | !cd demo && sh init.sh | _____no_output_____ | Apache-2.0 | demo/predict-taxi-trip-duration-nb/develop_ml_application_tour.ipynb | heiyan1shengdun/OpenMLDB |
导入行程历史数据到fedb使用fedb进行时序特征计算是需要历史数据的，所以我们将历史的行程数据导入到fedb，以便实时推理可以使用历史数据进行特征推理，导入代码可以参考https://github.com/4paradigm/DemoApps/blob/main/predict-taxi-trip-duration-nb/demo/import.py | !cd demo && python3 import.py | _____no_output_____ | Apache-2.0 | demo/predict-taxi-trip-duration-nb/develop_ml_application_tour.ipynb | heiyan1shengdun/OpenMLDB |
使用行程数据进行模型训练通过label数据进行模型训练，一下是这次任务使用的代码* 训练脚本代码 https://github.com/4paradigm/DemoApps/blob/main/predict-taxi-trip-duration-nb/demo/train_sql.py * 训练数据 https://github.com/4paradigm/DemoApps/blob/main/predict-taxi-trip-duration-nb/demo/data/taxi_tour_table_train_simple.snappy.parquet整个任务最终会生成一个model.txt | !cd demo && python3 train.py ./fe.sql /tmp/model.txt | _____no_output_____ | Apache-2.0 | demo/predict-taxi-trip-duration-nb/develop_ml_application_tour.ipynb | heiyan1shengdun/OpenMLDB |
使用训练的模型搭建链接fedb的实时推理http服务基于上一步生成的模型和fedb历史数据，搭建一个实时推理服务，整个推理服务代码参考https://github.com/4paradigm/DemoApps/blob/main/predict-taxi-trip-duration-nb/demo/predict_server.py | !cd demo && sh start_predict_server.sh ./fe.sql 8887 /tmp/model.txt | _____no_output_____ | Apache-2.0 | demo/predict-taxi-trip-duration-nb/develop_ml_application_tour.ipynb | heiyan1shengdun/OpenMLDB |
通过http请求发送一个推理请求整个请求很简单，具体代码如下```pythonurl = "http://127.0.0.1:8887/predict"req ={"id":"id0376262", "vendor_id":1, "pickup_datetime":1467302350000, "dropoff_datetime":1467304896000, "passenger_count":2, "pickup_longitude":-73.873093, "pickup_latitude":40.774097, "dropoff_longitude":-73.926704, "dropoff_latitude":40.85... | !cd demo && python3 predict.py | _____no_output_____ | Apache-2.0 | demo/predict-taxi-trip-duration-nb/develop_ml_application_tour.ipynb | heiyan1shengdun/OpenMLDB |
[Module 2.1] 피쳐 엔지니어링이 노트북은 아래와 같은 피쳐 엔지니어링을 통하여 새로운 피쳐를 생성 합니다.- 날짜관련 피쳐 생성(월, 일, 요일)- 기존의 피쳐들을 결합하여 새로운 피쳐 생성 (피쳐1 + 피쳐2 = 뉴피쳐)- Product_ID를 기준으로 Target Encoding 하여 새로운 피쳐 생성- Product_ID를 기준으로 Target Encoding Smoothing 하여 새로운 피쳐 생성- Category 피쳐를 레이블 인코딩 함- 로컬에 데이터 저장 - 최종 레이블 인코딩 된 데이터 세트 저장 (XGBoost, CatBoost 용)... | import pandas as pd
pd.options.display.max_rows=5
import numpy as np
%store -r full_data_file_name | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
데이터 로딩 및 셔플링 | df = pd.read_csv(full_data_file_name)
df = df.sample(frac=1.0, random_state=1000)
df
df.columns | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
날짜 피쳐 생성: Month, Day, WeeoOfDay(요일) | def create_date_feature(raw_df):
df = raw_df.copy()
df['order_date'] = pd.to_datetime(df['order_approved_at'])
df['order_weekday'] = df['order_date'].dt.weekday
df['order_day'] = df['order_date'].dt.day
df['order_month'] = df['order_date'].dt.month
return df
f_df = create_date_f... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
기존 피쳐 결합하여 새로운 피쳐 생성 (컬럼1 + 컬럼2 = 뉴피쳐) | def change_var_type(f_df):
df = f_df.copy()
df['customer_zip_code_prefix'] = df['customer_zip_code_prefix'].astype(str)
df['seller_zip_code_prefix'] = df['seller_zip_code_prefix'].astype(str)
return df
def comnbine_columns(f_df,src_col1, src_col2,new_col):
df = f_df.copy()
df[new_col] = df[... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
custoemr_state + seller_state | f_df = comnbine_columns(f_df,src_col1='customer_state', src_col2='seller_state',new_col='customer_seller_state') | df shape: (67176, 22)
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
custoemr_city + seller_city | f_df = comnbine_columns(f_df,src_col1='customer_city', src_col2='seller_city',new_col='customer_seller_city') | df shape: (67176, 23)
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
custoemr_zip + seller_zip | f_df = comnbine_columns(f_df,src_col1='customer_zip_code_prefix',
src_col2='seller_zip_code_prefix',new_col='customer_seller_zip_code_prefix')
f_df | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
product volume 컬럼 생성 (가로 * 세로 * 높이 의 계산값) | def add_product_volume(raw_df):
df = raw_df.copy()
df['product_volume'] = df.product_length_cm * df.product_width_cm * df.product_height_cm
return df
f_df = add_product_volume(f_df)
f_df.columns | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
Train, Test 데이터 셋 분리 |
def split_data_2(raw_df, sort_col='order_approved_at',val_ratio=0.3):
'''
train, test 데이터 분리
'''
df = raw_df.copy()
val_ratio = 1 - val_ratio # 1 - 0.3 = 0.7
df = df.sort_values(by= sort_col) # 시간 순으로 정렬
# One-Hot-Encoding
data1,data2, = np.split(df,
[int(va... | data1, data2 shape: (53740, 25),(13436, 25)
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
Target Encoding 관련 피쳐 생성- Product_ID 별 Classes의 평균, 갯수 (te_pdid_mean, te_pdid_count)- Target Error (classes - te_pdid_mean) Target Encoding with Smoothing아래 비디오 및 코드 참조 함- Feature Engineering - RecSys 2020 Tutorial: Feature Engineering for Recommender Systems - https://www.youtube.com/watch?v=uROvhp7cj6Q ... |
def create_target_encoding(cat, raw_df):
'''
te_mean, te_count 피쳐 생성
'''
df = raw_df.copy()
te = df.groupby(cat).classes.agg(['mean','count']).reset_index()
te_mean_col = 'te_' + cat + '_mean'
te_count_col = 'te_' + cat + '_count'
cat = [cat]
te.columns = cat + [te_mean_col,te_... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
Target Encoding 실행 | def add_new_te(raw_train, raw_test):
train_df = raw_train.copy()
test_df = raw_test.copy()
cat = 'product_id'
trn, sub = target_encode(train_df[cat],
test_df[cat],
target=train_df.classes,
min_samples_leaf=100... | (53740, 33)
(13436, 33)
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
Category 레이블 Encoding | # from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
class LabelEncoderExt(object):
'''
Source:
# https://stackoverflow.com/questions/21057621/sklearn-labelencoder-with-never-seen-before-values
'''
def __init__(self):
"""
It differs from LabelEncoder by ... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
Category 변수의 레이블 인코딩 실행 | label_cols = ['customer_city','customer_state','customer_zip_code_prefix']
train2_lb, test2_lb = make_test_label_encoding(train2_df, test2_df, label_cols)
pd.options.display.max_rows = 10
show_rows = 5
print(train2_lb.customer_state.value_counts()[0:show_rows])
# print(train2_lb[train2_lb.lb_customer_city == 185])
prin... | SP 28232
MG 6763
RJ 6034
PR 2912
RS 2385
Name: customer_state, dtype: int64
SP 6642
MG 1541
RJ 1491
PR 715
RS 663
Name: customer_state, dtype: int64
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
레이블 Encoding 안하고 바로 사용(AutoGluon 용) | # no_encoding_cate = tes_df | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
최종 사용할 컬럼 지정 XGBoost, CatBoost 알고리즘 용 | def filter_df(raw_df, cols):
df = raw_df.copy()
df = df[cols]
return df
cols = ['classes',
'lb_customer_city',
'lb_customer_state',
'lb_customer_zip_code_prefix',
'price', 'freight_value',
'product_weight_g',
'product_volume',
'order_w... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
피쳐 변환한 AutoGluon 용 | cols = ['classes',
'customer_city',
'customer_state',
'customer_zip_code_prefix',
'product_category_name_english',
'price', 'freight_value',
'product_weight_g',
'product_volume',
'order_weekday',
'order_day', 'ord... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
펴쳐 변환 없이 AutoGluon 용 | train_df.columns
cols = ['classes',
'customer_zip_code_prefix', 'customer_city', 'customer_state', 'price',
'freight_value', 'product_weight_g',
'product_category_name_english', 'seller_zip_code_prefix',
'seller_city', 'seller_state', 'order_date', 'order_weekday',
'order_day', 'ord... | _____no_output_____ | MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
로컬에 데이터 저장 | import os
def save_local(train_data, test_data, preproc_folder):
train_df = pd.concat([train_data['classes'], train_data.drop(['classes'], axis=1)], axis=1)
train_file_name = os.path.join(preproc_folder, 'train.csv')
train_df.to_csv(train_file_name, index=False)
print(f'{train_file_name} is saved')
... | Stored 'pre_train_file' (str)
Stored 'pre_test_file' (str)
Stored 'auto_train_file' (str)
Stored 'auto_test_file' (str)
Stored 'no_auto_train_file' (str)
Stored 'no_auto_test_file' (str)
| MIT | brazil_ecommerce/working/ref-te-method01-Feature_Engineer.ipynb | gonsoomoon-ml/predict-delivery-time |
1.Write a function contracting(l) that takes as input a list of integer l and returns True if the absolute difference between each adjacent pair of elements strictly decreases.Here are some examples of how your function should work. >>> contracting([9,2,7,3,1]) True >>> contracting([-2,3,7,2,-1]) False >>> contr... | def contracting(l):
n=len(l)
b=abs(l[1]-l[0])
for i in range(2,n):
d=abs(l[i]-l[i-1])
if (d<b):
b=d
else:
return False
break
return True
contracting([-2,3,7,2,-1]) | _____no_output_____ | Apache-2.0 | week3_assignment.ipynb | GunaSekhargithub/npteldatastructureswithpython |
2.In a list of integers l, the neighbours of l[i] are l[i-1] and l[i+1]. l[i] is a hill if it is strictly greater than its neighbours and a valley if it is strictly less than its neighbours.Write a function counthv(l) that takes as input a list of integers l and returns a list [hc,vc] where hc is the number of hills in... | def counthv(l):
a=[]
hc=0
vc=0
for i in range(1,len(l)-1):
if (l[i]>l[i-1] and l[i]>l[i+1]):
hc+=1
elif (l[i]<l[i-1] and l[i]<l[i+1]):
vc+=1
else:
continue
a.append(hc)
a.append(vc)
return a
counthv([3,1,2,3]) | _____no_output_____ | Apache-2.0 | week3_assignment.ipynb | GunaSekhargithub/npteldatastructureswithpython |
3.A square n×n matrix of integers can be written in Python as a list with n elements, where each element is in turn a list of n integers, representing a row of the matrix. For instance, the matrix 1 2 3 4 5 6 7 8 9would be represented as [[1,2,3], [4,5,6], [7,8,9]].Write a function leftrotate(m) that takes a l... | def col(l,n):
m=[]
for i in range(len(l)):
m.append(l[i][n])
return m
def leftrotate(l):
m=[]
for i in range(len(l)-1,-1,-1):
m.append(col(l,i))
return m
leftrotate([[1,2],[3,4]]) | _____no_output_____ | Apache-2.0 | week3_assignment.ipynb | GunaSekhargithub/npteldatastructureswithpython |
EDA Car Data Set**We will explore the Car Data set and perform the exploratory data analysis on the dataset. The major topics to be covered are below:**- **Removing duplicates**- **Missing value treatment**- **Outlier Treatment**- **Normalization and Scaling( Numerical Variables)**- **Encoding Categorical variables( D... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Loading the data set**We will be loading the EDA cars excel file using pandas. For this we will be using read_excel file.** | df=pd.read_excel('EDA Cars.xlsx') | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Basic Data Exploration **In this step, we will perform the below operations to check what the data set comprises of. We will check the below things:**- **head of the dataset**- **shape of the dataset**- **info of the dataset**- **summary of the dataset** **head function will tell you the top records in the data set. B... | # Converting Postel Code into Category | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**info() is used to check the Information about the data and the datatypes of each respective attributes.** **The describe method will help to see how data has been spread for the numerical values. We can clearly see the minimum value, mean values, different percentile values and maximum values.** Check for Duplicate ... | # Check for duplicate data
| Number of duplicate rows = 14
| MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**Since we have 14 duplicate records in the data, we will remove this from the data set so that we get only distinct records.** **Post removing the duplicate, we will check whether the duplicates has been removed from the data set or not.** | # Check for duplicate data
dups = df.duplicated()
print('Number of duplicate rows = %d' % (dups.sum()))
df[dups] | Number of duplicate rows = 0
| MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**Now, we can clearly see that there are no duplicate records in the data set. We can also quickly confirm the number of records by using the shape attribute as those 14 records should be removed from the original data. Initially it had 303 records now it should have 289** | df.shape | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Outlier Treatment**To check for outliers, we will be plotting the box plots.** | df.boxplot(column=['INCOME'])
plt.show()
df.boxplot(column=['TRAVEL TIME'])
plt.show()
df.boxplot(column=['CAR AGE'])
plt.show()
df.boxplot(column=['MILES CLOCKED'])
plt.show() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**Looking at the box plot, it seems that the three variables INCOME, MILES CLOCKED and TRAVEL TIME have outlier present in the variables.****These outliers value needs to be teated and there are several ways of treating them:** - **Drop the outlier value**- **Replace the outlier value using the IQR** **Created a use... | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba | |
Make Boxplots after Outlier Treatment | df.boxplot(column=['TRAVEL TIME'])
plt.show()
df.boxplot(column=['MILES CLOCKED'])
plt.show() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**If you look at the box plots above,post treating the outlier there are no outliers in all these columns.** Check for missing value | # Check for missing value in any column
| _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**We can see that we have various missing values in respective columns. There are various ways of treating your missing values in the data set. And which technique to use when is actually dependent on the type of data you are dealing with.**- **Drop the missing values : In this case we drop the missing values from thos... | df[df.isnull().sum()[df.isnull().sum()>0].index].dtypes | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
**Replacing NULL values in Numerical Columns using Median** | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba | |
**Replacing NULL values in Categorical Columns using Mode** | # Check for missing value in any column
df.isnull().sum() | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
Univariate Analysis | # histogram of income | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
From above figure, we can say that the Income parameter is right skewed | sns.countplot(df["EDUCATION"],hue=df["SEX"]) #countplot for Education wrt SEX | _____no_output_____ | MIT | M3 Advance Statistics/W2 EDA/EDA_Cars_Student_File.ipynb | fborrasumh/greatlearning-pgp-dsba |
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