cindyangelira

def data_exploration():
    df = df1.copy()
    st.title("Data Exploration via Pandas Profiling", anchor="data-exploration")
    st.caption("Please wait for a few seconds for the data exploration to be completed")
    df_profile = ProfileReport(df, explorative=True)
    st_profile_report(df_profile)

cindyangelira

# Set page config
st.set_page_config(
    page_title="Customer Behavior Analysis",
    page_icon="📊",
    layout="wide"
)

st.title("Customer Behavior Analysis", anchor="customer-behavior-analysis")
# Upload file
uploaded_file = st.file_uploader("Please Import Your Transaction Data", type="csv")

cindyangelira

side = st.sidebar.selectbox("Select your page", ["Home", "Data Exploration", "RFM Analysis", "Market Basket Analysis", "Customer Lifetimes Value"])
if side == "Home":
    home()
elif side == "Data Exploration":
    data_exploration()
elif side == "RFM Analysis":
    RFM()
elif side == "Market Basket Analysis":
    market_basket()
else:

cindyangelira

def encode_units(x):
        if x <= 0:
            return 0
        else:
            return 1

def mba(df, support):
    df1 = df.copy()
    df2 = df1[df1.sum(axis=1) > 1]
    frequent_itemsets = apriori(df2.astype('bool'), min_support=support, use_colnames=True)

cindyangelira

def rfm_segmentation(df, technique:int, num_segments:int):
    if technique == 1:
        data = df.copy()
        # K-means clustering
        X_ = data[['recency', 'frequency', 'monetary']]
        X = StandardScaler().fit_transform(X_)
        kms = KMeansInterp(
	    n_clusters=num_segments, random_state=42,
	    ordered_feature_names=X_.columns.tolist(), 
	    n_init = 'auto', max_iter = 1000,

cindyangelira

steps = 10**6
transition = model_try.prob_matrix
transition_n = transition
for i in range(steps):
    transition_n=np.matmul(transition_n,transition)
print("pi = ",transition_n[1])

cindyangelira

class MarkovChainPred(object):
    def __init__(self, transition_matrix, states):
        """
        Initialize the MarkovChain instance.

        Parameters
        ----------
        transition_matrix: 2-D array
            A 2-D array representing the probabilities of change of 
            state in the Markov Chain.

cindyangelira

# Set up python env in R----
library(survivalmodels)
install_pycox(pip = TRUE, install_torch = TRUE)
install_keras(pip = TRUE, install_tensorflow = TRUE)

cindyangelira

# Plot all survival curves----
plot(coxtime$prediction(predict_sets = "test")$distr, ind = 1, fun = "survival")

# Plot first two survival curves----
plot(coxtime$prediction(predict_sets = "test")$distr[1], "survival", main = "First 2 Survival Curves")
lines(coxtime$prediction(predict_sets = "test")$distr[2], "survival", col = 2)

cindyangelira

coxtime = aggr$resample_result[[1]]
coxtime$prediction(predict_sets = "test")