CHNMSCS

import numpy as npimport matplotlib.pyplot as pltimport tensorflow as tfobservations = 1000xs = np.random.uniform(low=-10, high=10, size=(observations,1))zs = np.random.uniform(-10,10,(observati...

# import the relevant librariesimport numpy as np # mathematical operationimport matplotlib.pyplot as plt # nice graphsfrom mpl_toolkits.mplot3d import Axes3D # Nice 3D graphs# Generate rand...

Creating a machine learning algorithm means building a model that output correct information.

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snssns.set()from sklearn.cluster import KMeansdata = pd.read_csv('3.12. Example.csv')print(data)代码紧跟上面...

""" 1. Cluster analysis is a multivariate statistical technique that groups observations on the basis some of their features or variables they are described by. 2. Observations in a data set can be ...

import numpy as npimport pandas as pdimport statsmodels.api as smimport matplotlib.pyplot as pltimport seaborn as snssns.set()raw_data = pd.read_csv('2.01. Admittance.csv')print(raw_data)prin...

直接就进入代码环节了哈！由于我在pycharm 上写的代码有点长，展示的结果有点很多。为了给各位看的清楚，就分段进行展示程序和结果这是头文件需要的库import numpy as npimport pandas as pdimport statsmodels.api as smimport matplotlib.pyplot as pltfrom sklearn.linear_mode...

The advantages of Scikit - learn:[1] Incredible documentation[2] Variety (Regression, Classification, Clustering, Support vector machines, Dimensionality reduction).Numerical stability: the basic ...

之前已经用了python来画 matplotlib 的默认风格图形，现在还是用之前的表格来画Seaborn. 还是老规矩，直接上代码import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snssns.set() #overrides the style and the graphics of all ma...

我们直接上代码了，之前已经介绍了如何导入表格了，现在就用表格中的数据来进行画图：还是用之前的表格import pandas as pdimport matplotlib.pyplot as pltdata = pd.read_csv('1.01. Simple linear regression.csv')y = data('GPA')x = data('SAT')plt.sca...

刚刚博主学到如何用Python来导入表格，现在就将过程写给大家看看。我是用了Pycharm 的2019.3.1 版本的，这还得需要下载Python 3.7 Interpreter 才能运行。这个可是弄了很久才弄好的。不说那么多了，直接上代码：import pandas as pddata = pd.read_csv('1.01. Simple linear regression.csv')...

There are four steps in data driven decision making. First: Formulate a hypothesis, Second: Find the right test for your hypothesis, Third: Execute the test, Fourth: Make a decision based on the resu...

Confidence interval is much more accurate representation of reality.The level of confidence. It is denoted by one minus Alpha and is called the confidence level of the interval. Alpha is a value bet...

Inferential Statistics refers to methods that rely on probability theory and distributions in particular to predict population values based on sample data.In statistics when we use the term distribu...

Population: It is the collection of all items of interest. Denote with an upper case n the numbers we have obtained when using a population are called parameters.Sample: It is a subset of the popula...

For starts in finance we are often trying to predict the values and prices of uncertain future events and easy to understand event is option pricing before we proceed we need to understand what an op...

Continuous Distribution: Sample space is infinite, and We cannot record the frequency of each distinct value. Thus we can no longer represent these distributions with a table what we can do is repres...

Bernoulli distribution: any event where we only have one trial and two possible outcomes follows such a distribution.The graph of a Bernoulli distribution is simple. It consists of two bars one for ...

Additive law: The probability of the union of two sets is equal to the sum of the individual probabilities of each event minus the probabilityy of their intersection.Bayes’ Rule: The condition proba...

Combinatorics deals with combinations of objects from a specific finite set. These restrictions can be in terms of repetition, orther, or a different criterion.Use Permutations, Variations and Combi...

Probability is the chance of something happening. A more academic definition for this would be likelihood of an event occuring. The word event has a specific meaning when talking about probabilities....

Programming language enables you to devise programs that can execute specific operations. Moreover you can reuse these programs whenever you need to execute the same action.Python and R language are...

What situation is it preferable to make predictions using machine learning rather than traditional statistical methods.Machine learning: The core to create an algorithm which a computer then uses to...

You have prepared the BMI reports and dashboards and the executives have extracted insights about the business what do you do with the information you use it to predict some futuer values as accurate...

Business intelligence (BI) analysis need to use data skills and business knowledge & intuition. A good business intelligence analyst does not look at a problem from only one angle. The job of a b...

In the Big data, it can be text data, digital image data, digital video data, digital audio data and more. Consequently with a larger amount of data types comes a wider range of data cleansing method...

Raw data is called it wrong facts or primary data is data which cannot be analyzed straight away. It is untouched data you have accumulated and stored on the server. The gathering of raw data is refe...

Data: It is defined as information stored in a digital format, which can then be used as a base for performing analyses and decision making.Traditional Data: Data in the form of tables containing nu...

Business Intelligence (BI): the process of analysing and reporting historical business data after reports and dashboards have been prepared. They can be used to make an informed strategic and technic...

Business world: Business Cose, Qualitative Analytics, Preliminary Data Report, Reporting with Visuals, Creating Dashboards and Sales Forecasting.Some business activities are data-driven while others ...

Analysis:Consider you have a huge data set containing data of various types. Instead of tackling the entire dataset and running the risk of becoming overwhelmed you separated into easier to digest ch...

What’s so important about data in this day. Age maintaining a healthy business goes hand-in-hand alongside working with data whether you understand it or not. There is no denying that data is that the...