This is a simple analysis of Pokemon dataset from Kaggle using Python , NumPy, Pandas, and Matplotlib. numpy python3 matplotlib dataanalysis pandas-python kaggledatasets pokemon-dataset Updated Oct 8, 2020 Jupyter Notebook Hasibwajid / SKETCH_TO_IMAGE_USING_CGAN Star 2 Code Issues Pull...
import re deck = {"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "U": 8} data = [train_df, test_df] for dataset in data: dataset['Cabin'] = dataset['Cabin'].fillna("U0") dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile("([A-...
Dataset(x_train, y_train, weight=w_train) d_valid = lgb.Dataset(x_valid, y_valid, weight=w_valid) partial_bst = lgb.train(self.params, d_train, 10000, valid_sets=d_valid, early_stopping_rounds=50) num_round = partial_bst.best_iteration d_all = lgb.Dataset(X, label = y, ...
The data used in this report is downloaded fromKaggle’sHeart Failure Prediction Dataset(Heart Failure Prediction Dataset | Kaggle). This dataset has created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 ...
Missing values in train dataset. # Let's write a functin to print the total percentage of the missing values.(this can be a good exercise for beginners to try to write simple functions like this.) def missing_percentage(df): """This function takes a DataFrame(df) as input and returns...
traindata, trainlabel, testdata = load_dataset(trainfile, testfile) # print(traindata.shape[1]) # 11 linear_regression_train(traindata, trainlabel, testdata) # linear_regression_test(traindata, trainlabel, testdata) 2.逻辑回归模型
The dataset contains the following 21 columns: customerID: A unique ID that identifies each customer. gender: The customer's gender (Female, Male). SeniorCitizen: Whether the customer is a senior citizen or not (1, 0). Partner: Whether the customer has a partner or not (Yes, No). ...
4. Conduct a simple linear regression analysis that examines the effect of an independent variable on a dependent variable. 4.1. Interpret the model result. 4.2. Interpret the effect of the independent variable on the dependent variable....
model = LogisticRegression() # evaluate model scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) return scores # create dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=1) ...
通过调用 SimpleExpSmoothing 包,可以使用 EWMA, Exponentially Weighted Moving Average方法——一种单指数平滑方法。 使用EWMA 方法,我们首先需要定义 span 变量——数据集的季节周期。 fig, ax = plt.subplots(figsize=(15, 3)) plot_acf(data['demand'].tolist(), lags=60, ax=ax); ...