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Returns the confusion matrix for your ML model. (For Logistic regression model)
Input as - confusionMatrix(model, x, y)
where model = logistic regression model. x = input, and y = target

Returns the R2 of the ML model. Input as - r2(model, x , y)
1. model - linear model
2. x - reshaped input array
3. y - target data array

Returns the adjusted R2 of the ML model. Input as => adjustedR2(model, x, y)

Returns the bias of the model.
Input the trained model as bias(model) [Linear model preferrably]

Returns the weight(s) of the features.
Input the trained model as weights(model) [Linear model preferrably]

Provides you with the report of your Regression which contains info like weights, bias, p-value, etc as a dataframe.
Input x and y (reshaped)

Returns scatter plot of the input x and y along with their labels, title, etc. Input in sequence as-
scatterPlot( x, y, xlabel, ylabel, title)

Returns the distribution of the feature passed. Please ensure the array is optimally reshaped.

Returns the p-value of the model.
Input the X and Y (reshaped)

Returns the f-stats of the model.
Input the X and Y (reshaped)

Returns logistic report as a dataframe. Input x and y (reshaped)

Returns a dataframe that shows the summary of the Linear regression model.
Input as - summaryTable(x, y)
x = input data (with feature names), y = target data

Function to perform the clustering of the provided dataset using KMeans algorithm. Please input the following
x = data (reshaped), k = No. of clusters

Returns the graph of the elbow method showing WCSS for number of clusters.
Input the training data only.
NOTE - The number of cluster value K is preset i.e. the function calculates WCSS for clusters from 1 to 10 clusters

Returns the trained Linear Regression model.
Input as - LinearRegression( x , y )
where x = input data, y = target data
Note: Please ensure data format is compatible.

Returns the trained Logistics Regression model.
Input as - LogisticRegression( x , y )
where x = input data, y = target data
Note: Please ensure data format is compatible.

Saves the given data file in NPZ format.
Input in the given sequence = (filename, x, y)

Saves the given data file in CSV format.
Input in the given sequence = (filename, data)

Performs feature scaling on the given training data x.
It returns-
1. Scaled train data as scaled_x
2. scaler (StandardScaler()) by which the data is scaled

Returns the accuracy of your ML model. (For Logistic regression model)
Input as - logisticModelAccuracy(model, x, y)
where model = logistic regression model. x = input, and y = target

To create your basic Neural Network for classification (It is advised to try to train your own NN for better results)
Input keyworded arguments as:
1. x = training data aka input
2. y = target data
3. t = target size (output nodes in output layer)
4. e = Epochs
NOTE: The NN uses SOFTMAX activation function for its only layer. loss function = 'mean_squared_error' optimizer = 'sgd'

To find the probability of an event. Please input the keyworded arguments as s = sample space, e = favourable event
It returns the probability of the event, between 0 and 1.

Function to check if two events are independent or not.

Please input the following:
1. Set of event A and event B in LIST datatype as "a" and "b", otherwise Probability of A intersection B as "pab"
2. Probabily of event A as pa and event B as pb. This function returns TRUE if events are independent, otherwise FALSE

Function to return the frequency (in numbers) of an event in a given list of events.
It returns the frequency of given event as a positive integer. Please do make sure to input the events in LIST.

Function to generate a frequency distribution table. Returns a dictionary with keys as list data items and values as their respective frequency.
Please enter the data in list format.
for [ 'a' , 'a' , 'b' , 'c' , 'd' ]
returns { 'a' : 2 , 'b' : 1 , 'c' : 1 , 'd' : 1 }

Function to generate a RELATIVE frequency distribution table. Returns a dictionary with keys as list data items and values as their respective relative frequency.
Please enter the data in list format.
for [ 'a' , 'a' , 'b' , 'b' ]
returns { 'a' : 0.5 , 'b' : 0.5 }

Function to return the compliment of an event. Please input the probability of the event as argument.

Returns the graph of Probability Distibution Function. Input the data as an attribute (LIST)

Function to return the conditional probability i.e. P(a|b). Please input keyworded arguments as
CASE A: 'pab' Probability of A intersection B and 'pb' Probability of event B with 's' as sample space
NOTE: If you don't have pab and pb, then enter
CASE B: 'a' List of set A and 'b' List of set B with 's' as sample space
It will return the INT/FLOAT value which is result of P(a|b)

Returns the mean value of the sample presented as a LIST

Returns the mode value of the sample presented as a LIST

Returns the median value of the sample presented as a LIST

Returns the variance of the SAMPLE presented as a list

Returns the variance of the POPULATION presented as a list

Returns the standard deviation of Sample passed as a list

Returns the standard deviation of population passed as a list

Returns the covariance between two features x and y passed as a list

Returns the correlation between two features x and y passed as a list

Function to generate an Arithmatic Progression. Please pass a (First term), d (difference) and n (number of terms) as keyworded arguments. If you do not pass anything, the function will return an Arithmatic progression of 10 terms with random a and d.

Function to check if a series is an Arithmatic progression or not. Pass the series in form of LIST as argument. The output will be a boolean value i.e. TRUE or FALSE.

Function to find the nth element of an Arithmatic Progression. Please provide a (First term), d (difference) and n (number of terms) as keyworded arguments.

Function to determine the n. Please provide a (First term), d (difference) and an (the nth term) as keyworded arguments.

Function to determine the difference d of an Arithmatic Progression. Please provide a (First term), n (number of terms) and an (the nth term) as keyworded arguments.

Function to determine the sum of an Arithmatic Progression. Please provide a (First term), n (number of terms) and d (difference) as keyworded arguments.

Function to return the roots alpha and beta (A quadratic equation only has 2 roots) of the quadratic equation. It returns a Dictionary with keys alpha and beta as roots. Please provide a (coefficint of x² ), b (coefficient of x) and c(constant term) as keyworded arguments.

Function to verify if the given number is a perfect square or not, returns Boolean

Function to return the type of roots the quadratic equation has. Please provide a (coefficint of x² ), b (coefficient of x) and c(constant term) as keyworded arguments.