CoolFace
Modelpublic

Ediashta/HaiMeds_Churn_Prediction

sourceHugging Faceupdated 3y agoView on Hugging Face
0likes1downloads
prediction.cpython-310.pyc52 linesDownload Raw Back to __pycache__
1o

2g"�d��@s�ddlZddlZddlZddlZddlmZe	dd��
Z3e�e4�ZWd�n1s+wYed�Z
dd�ZedkrAe�dSdS)	�N)�5load_modelz./column_transformer.pkl�rbz./functional_model.kerascCs�t�d���t�d�t�d�tjddd�\}}|jddd	d6d�}|jdd
d�}t�d�tjddd�\}}}}|jdddd�}|jdddd�}|jdddd�}|jddd�}	t�d�t�d�tjddd�\}}}}}7|jdd d	d!d�}|jd"d#d$d%�}|jd&d'd(d%�}
|jd)d*d+�}|8jd,d-d.d/�}t�d�tjd0dd�\}}}|jd1d2d�}|jd3dd�}|jd4dd�}t�d�tjd0dd�\}}}|jd5dd�}|jd6d7d�}|jd8d9d�}t�d:�}Wd�n1s�wYid;|�d<|�d=|�d>|�d?|�d@|�dA|	�dB|�dC|�dD|�dE|
�dF|�dG|�dH|�dI|�dJ|�dK|�g}t	�9|�}t�|�t�
|�}t�|�}t�|dLkd	dM�}t�dN�|dMd	k�radO}tj|dPdQ�t�dR�dSdS}tj|dPdQ�t�dT�dS)UNzkey=churn_predictionzChurn Score Predictionz**Customer Data**��large)�gap�AgezCustomer Age��)�label�help�step�valuezMembership Category)z
No MembershipzBasic MembershipzPremium MembershipzSilver MembershipzGold MembershipzPlatinum Membership)r10�optionsz---��RegionzCustomer Residence Region)�Town�City�Village)r11rr�ReferralzJoined Through Referral?)�Yes�Noz	Device(s)zDevice Used)�12Smartphone�Desktop�BothzInternet Connection)zWi-Fi�Fiber_Optic�Mobile_Dataz**Customer Behavior**�z13Last LoginzDays Since Last Login�zAvg. Usage TimezAverage Usage Time (Minutes)�)r14rr
zAvg. Login FrequencyzAverage Login Frequency (Days)�zPoints in Walleti,)r15r
zAvg. Transaction�d�USD)r16r
r�zPreferred Offer Type)zGift Vouchers/CouponszCredit/Debit Card OfferszWithout OfferszUsed Discount Before?zApplication Preference Offer?zPast Complaint?zComplaint Status)zNot Appllicable�Unsolved�SolvedzSolved in Follow-upzNo Information Availablez
Feedback Type)�Neutral�Positive�Negative�Predict�age�region_category�membership_category�joined_through_referral�preferred_offer_types�medium_of_operation�internet_option�days_since_last_login�avg_time_spent�avg_transaction_value�avg_frequency_login_days�points_in_wallet�used_special_discount�offer_application_preference�past_complaint�complaint_status�feedbackg�������?rz*Prediksi Churn Pelanggan Tersebut adalah :a17                    <style>18                    p.a {19                    font: bold 36px Arial;20                    color: teal;21                    }22                    </style>23                    <p class="a">Pelanggan Tidak Berpotensi Churn</p>24                    T)�unsafe_allow_htmlzIDapat menekankan program loyalty agar pelanggan tetap menggunakan layanana	25                    <style>26                    p.a {27                    font: bold 36px Arial;28                    color: red;29                    }30                    </style>31                    <p class="a">Pelanggan Berpotensi Churn</p>32                    z7Dapat diberikan promosi untuk menarik pelanggan kembali)�st�form�	subheader�markdown�columns�number_input�	selectbox�radio�form_submit_button�pd�	DataFrame�	dataframe�column_transformer�	transform�model_functional�predict�np�where�write)�col1�col2r)�33membership�col3�col4�region�referral�device�internet�col5�34last_login�avg_time�	avg_login�points�transaction�35offer_pref�	used_disc�	offer_app�36complaints�complaints_statusr9�	submitted�data_inf�data_inf_transform�37y_pred_inf�html_str�rg�tD:\Kuliah\Hackitv8 - Data Scientist\04. Phase 2\02. Mileston 01\p2-ftds020-rmt-ml1-ediashta\deployment\prediction.pyrJs�3839�40����4142���43�	�44�45��j��������	�46���
������4748495051	�	rJ�__main__)�	streamlitr;�pandasrD�numpyrK�pickle�tensorflow.keras.modelsr�open�file_1�loadrGrIrJ�__name__rgrgrgrh�<module>s�'52�