
#-----------------------------------------------------------------------------------------------------------------------------
#----------------------------------------------[ Needed Libraries Import ]----------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------


import easygui
import sweetviz as sv
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from ydata_profiling.controller.pandas_decorator import profile_report
import plotly.graph_objects as go


#-----------------------------------------------------------------------------------------------------------------------------
#---------------------------------------------------[ Import DataBase ]-------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------


dataTelecom=pd.read_excel('S:\\TunisieTelecom\\TelecomData.xlsx')
dataTelecom.head(10)


dataTelecomSample = dataTelecom.sample(n=1000)


#-----------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------[ Variables Profiling ]----------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------


dataTelecom.describe()


# Assuming your dataframe is called 'df'
selected_variables = [
'DUREE_APPEL_TOT',
'nb_sms_tot' ,
'revenu_cdr_c',
'MNT_RECH',
'FREQ_ACT_OUT',
'NB_JOUR_APPEL_TAXE' ,
'NB_SMS_TAXE' ,
'NB_RECH_SUP5',
'MNT_TRANSFERT_OUT' ,
'revenu_voix' ,
'MNT_FORFAIT_DATA',
'NB_FORFAIT_VOIX' ,
'OFFRE',
'VOLUME_SESSION',
'P_revenu_data',
'MNT_FORFAIT',
'FREQ_USSD',
'P_FF_Data' ,
'Duree_onnet_tot',
'Duree_offnet_tot' ,
]
# Select the desired variables from the dataframe
dataTelecomSelected = dataTelecom[selected_variables].copy()
dataTelecomSelected.head(100)


nan_counts = dataTelecomSelected.isna().sum(axis=1)
num_rows_with_nan = (nan_counts > 0).sum()
print("Number of rows with NaN values:", num_rows_with_nan)

Number of rows with NaN values: 0


profile=profile_report(dataTelecomSelected)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


#-----------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------[ Label Encoder ]----------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------


# Create an instance of LabelEncoder
label_encoder = LabelEncoder()
dataTelecomEncoded=dataTelecom.copy()
# Fit and transform the column you want to encode
encoded_statut = label_encoder.fit_transform(dataTelecomEncoded['STATUT'])
encoded_offre = label_encoder.fit_transform(dataTelecomEncoded['OFFRE'])
encoded_handset = label_encoder.fit_transform(dataTelecomEncoded['HANDSET'])

# Replace the original column with the encoded values
dataTelecomEncoded['STATUT'] = encoded_statut
dataTelecomEncoded['OFFRE'] = encoded_offre
dataTelecomEncoded['HANDSET'] = encoded_handset
dataTelecomEncoded.head(20)


#-----------------------------------------------------------------------------------------------------------------------------
#--------------------------------------------------[ Correlation Matrix ]----------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------


correlation_matrix = dataTelecomEncoded.corr()


fig = go.Figure(data=go.Heatmap(z=correlation_matrix.values,
                                x=correlation_matrix.columns,
                                y=correlation_matrix.index))

# Save the figure as a PNG file
fig.update_layout(height=800)
fig.write_html("correlation_matrix13.html")
fig.show()


#-------------------------------------------------------------------------------------------------------------------------
#---------------------------------------    Correlation Matrix > 0.5     -------------------------------------------------
#0.5 to 0.7: Substantial (significant) correlation | 0.7 to 0.9: Strong correlation | 0.9 to 1.0: Very strong correlation
#-------------------------------------------------------------------------------------------------------------------------


# Filter the correlation matrix
filtered_matrix = correlation_matrix[correlation_matrix > 0.5]

fig = go.Figure(data=go.Heatmap(z=filtered_matrix.values,
                                x=filtered_matrix.columns,
                                y=filtered_matrix.index))

# Update the layout to increase the height
fig.update_layout(height=600)

# Save the figure as a PNG file
fig.show()

	STATUT	OFFRE	ANC_M	HANDSET	revenu_voix	revenu_inter	NB_JOUR_ACTIVITE_TAXE	NB_JOUR_APPEL_TAXE	DUREE_APPEL_TOT	DUREE_APPEL_TAXEE	...	FREQ_USSD_VOIX	FREQ_USSD_SMS	VOLUME_SESSION	VOLUME_SESSION_WEEKEND	REVENU_VAS	ARPU	P_revenu_data	P_revenu_voix_c	P_revenu_vas_c	id_client
0	Active	Offre30	123	2G	12.709375	1.464447	10.366042	10.658842	27.261836	2.125971	...	28.679570	146.930634	200000.000000	54.025577	1.059822	4.325398	0.136685	0.943095	0.279541	1.0
1	Active	Offre8	98	2G	3.000000	0.756078	1.330736	7.079320	19.883099	7.735475	...	28.490597	169.219363	3337.992419	24.789260	1.411803	2.677563	0.533695	0.540374	0.712010	2.0
2	Active	Offre24	90	4G	32.514156	0.681197	0.158160	4.367702	133.476368	7.681088	...	190.000000	44.403308	107082.775926	160.031496	18.195224	1.954007	0.799606	0.071368	0.376754	3.0
3	Active	Offre10	226	2G	3.821551	5.265345	4.003452	3.086766	35.053364	8.540951	...	19.412661	190.000000	128700.752169	102.668979	17.746873	2.496790	0.426526	0.314082	0.676112	4.0
4	Active	Offre1	139	2G	60.009385	1.957144	5.689241	0.403300	0.844956	12.355853	...	81.567146	6.365788	75654.384291	0.341570	7.280861	2.291381	0.319736	0.600057	0.719450	5.0
5	Suspended	Offre16	118	2G	3.000000	1.636158	9.406248	3.346885	26.779409	14.407004	...	34.922712	40.620738	65253.482895	33.274496	10.439648	6.212364	0.274582	0.419963	0.280881	6.0
6	Active	Offre10	154	2G	31.953274	1.656711	3.993698	5.357250	2.113332	20.677427	...	34.152966	95.329383	45157.810902	41.821374	0.249189	1.432389	0.566405	0.152414	0.130876	7.0
7	Suspended	Offre6	96	2G	10.331387	2.498662	16.020874	0.100707	5.922602	7.850522	...	65.508907	75.616780	37165.463625	65.573652	1.780176	3.982989	0.620034	0.369095	0.722737	8.0
8	Active	Offre23	112	2G	14.262607	8.888795	0.773295	2.281113	68.691901	0.827132	...	190.000000	28.773361	200000.000000	35.931179	0.290472	1.772127	0.087045	0.512316	0.922651	9.0
9	Active	Offre8	137	2G	5.870684	0.824999	3.084907	11.425205	37.872839	19.419044	...	50.228614	89.822382	65233.389597	36.545510	0.035157	0.874001	0.708388	0.766209	0.142649	10.0

	ANC_M	revenu_voix	revenu_inter	NB_JOUR_ACTIVITE_TAXE	NB_JOUR_APPEL_TAXE	DUREE_APPEL_TOT	DUREE_APPEL_TAXEE	DUREE_APPEL_GRATUIT	nb_APPEL_GRATUIT	nb_APPEL_TAXE	...	FREQ_USSD_VOIX	FREQ_USSD_SMS	VOLUME_SESSION	VOLUME_SESSION_WEEKEND	REVENU_VAS	ARPU	P_revenu_data	P_revenu_voix_c	P_revenu_vas_c	id_client
count	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	...	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	92143.000000	9.214300e+04	92142.000000
mean	149.171364	15.239636	1.498722	7.809857	6.874736	99.955509	15.062154	15.062154	30.006924	14.912332	...	79.053113	79.064533	86696.838371	100.244944	5.011416	4.979407	0.501161	0.499919	5.008529e-01	46071.500000
std	57.765160	14.682741	1.497972	7.283316	6.526537	99.949019	15.022923	15.022923	29.974072	14.947696	...	61.947681	61.913723	66338.180671	99.833913	5.026841	4.997452	0.288310	0.288800	2.891844e-01	26599.248589
min	50.000000	3.000000	0.000001	0.000112	0.000068	0.000018	0.000240	0.000240	0.000261	0.000060	...	0.003447	0.000055	2.689071	0.000994	0.000015	0.000009	0.000001	0.000015	9.336428e-07	1.000000
25%	99.000000	4.276194	0.431270	2.283187	2.010586	28.798354	4.329003	4.329003	8.679738	4.281366	...	25.926583	25.960899	29194.406876	29.182456	1.427911	1.429946	0.251467	0.249248	2.503687e-01	23036.250000
50%	149.000000	10.358993	1.042991	5.493399	4.870998	69.419866	10.499201	10.499201	20.703940	10.333792	...	62.154732	62.309948	69415.161210	69.773265	3.450904	3.443868	0.502028	0.498710	5.013815e-01	46071.500000
75%	199.000000	20.725777	2.078414	11.126519	9.664099	137.938430	20.898580	20.898580	41.583846	20.680499	...	124.685338	124.695080	138643.945475	138.925907	6.956149	6.895372	0.750763	0.749972	7.525205e-01	69106.750000
max	249.000000	168.860618	19.976912	30.000000	30.000000	1155.277214	164.188009	164.188009	368.313521	171.425214	...	190.000000	190.000000	200000.000000	1222.280603	60.850935	51.063309	0.999988	1.000000	9.999926e-01	92142.000000

	DUREE_APPEL_TOT	nb_sms_tot	revenu_cdr_c	MNT_RECH	FREQ_ACT_OUT	NB_JOUR_APPEL_TAXE	NB_SMS_TAXE	NB_RECH_SUP5	MNT_TRANSFERT_OUT	revenu_voix	MNT_FORFAIT_DATA	NB_FORFAIT_VOIX	OFFRE	VOLUME_SESSION	P_revenu_data	MNT_FORFAIT	FREQ_USSD	P_FF_Data	Duree_onnet_tot	Duree_offnet_tot
0	27.261836	3.685763	19.941537	12.306756	10.667904	10.658842	1.624059	2.00	8.877510	12.709375	2.059265	8.589441	Offre30	200000.000000	0.136685	10.882398	190.000000	0.498091	30.583118	27.219712
1	19.883099	1.476121	3.908449	10.348607	13.650949	7.079320	7.059615	0.67	1.633333	3.000000	20.025208	5.685491	Offre8	3337.992419	0.533695	1.049276	18.623378	0.096339	13.426765	32.571522
2	133.476368	6.162948	32.541849	3.939266	7.079317	4.367702	1.638128	0.00	0.011593	32.514156	5.397060	4.417241	Offre24	107082.775926	0.799606	4.041068	66.449461	0.667540	6.217262	24.861880
3	35.053364	1.448782	4.145009	2.408488	17.423588	3.086766	0.016726	0.33	2.003034	3.821551	0.534564	0.107662	Offre10	128700.752169	0.426526	6.441283	19.259807	0.407815	5.837191	37.779013
4	0.844956	1.131308	60.496821	10.870592	0.067246	0.403300	3.934964	0.33	2.755649	60.009385	0.031219	2.276588	Offre1	75654.384291	0.319736	0.805758	38.034894	0.828712	10.395156	2.831625
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	1.826910	3.198013	5.638954	5.605779	16.488487	2.872333	0.420523	0.00	2.242692	3.000000	7.558205	8.874387	Offre8	32664.259485	0.857627	2.735164	41.723923	0.075043	2.354877	6.515707
96	80.858548	11.867689	8.145327	9.174032	13.042782	0.298674	0.329446	0.33	11.978245	3.000000	31.046581	2.124691	Offre2	82909.748831	0.980014	6.002234	177.422094	0.971460	0.183394	49.746824
97	107.330103	12.026432	6.377507	16.425132	2.520254	6.400014	0.631907	1.33	3.024095	4.531545	4.918999	1.420871	Offre10	141996.977868	0.772398	1.815659	76.287668	0.558104	121.321271	2.314348
98	86.011908	4.033539	20.611835	0.200019	0.963337	10.629819	0.790306	2.67	16.973030	11.762926	17.817781	7.554069	Offre15	12052.670191	0.152620	12.026577	143.305932	0.605920	15.517343	28.809924
99	89.864928	0.624341	22.949669	2.234320	0.512487	0.184009	4.610618	0.00	3.720673	17.606096	0.793076	0.566853	Offre30	134081.459601	0.893336	2.228678	79.240784	0.822927	78.564971	44.972508

	STATUT	OFFRE	ANC_M	HANDSET	revenu_voix	revenu_inter	NB_JOUR_ACTIVITE_TAXE	NB_JOUR_APPEL_TAXE	DUREE_APPEL_TOT	DUREE_APPEL_TAXEE	...	FREQ_USSD_VOIX	FREQ_USSD_SMS	VOLUME_SESSION	VOLUME_SESSION_WEEKEND	REVENU_VAS	ARPU	P_revenu_data	P_revenu_voix_c	P_revenu_vas_c	id_client
0	0	23	123	0	12.709375	1.464447	10.366042	10.658842	27.261836	2.125971	...	28.679570	146.930634	200000.000000	54.025577	1.059822	4.325398	0.136685	0.943095	0.279541	1.0
1	0	28	98	0	3.000000	0.756078	1.330736	7.079320	19.883099	7.735475	...	28.490597	169.219363	3337.992419	24.789260	1.411803	2.677563	0.533695	0.540374	0.712010	2.0
2	0	16	90	2	32.514156	0.681197	0.158160	4.367702	133.476368	7.681088	...	190.000000	44.403308	107082.775926	160.031496	18.195224	1.954007	0.799606	0.071368	0.376754	3.0
3	0	1	226	0	3.821551	5.265345	4.003452	3.086766	35.053364	8.540951	...	19.412661	190.000000	128700.752169	102.668979	17.746873	2.496790	0.426526	0.314082	0.676112	4.0
4	0	0	139	0	60.009385	1.957144	5.689241	0.403300	0.844956	12.355853	...	81.567146	6.365788	75654.384291	0.341570	7.280861	2.291381	0.319736	0.600057	0.719450	5.0
5	1	7	118	0	3.000000	1.636158	9.406248	3.346885	26.779409	14.407004	...	34.922712	40.620738	65253.482895	33.274496	10.439648	6.212364	0.274582	0.419963	0.280881	6.0
6	0	1	154	0	31.953274	1.656711	3.993698	5.357250	2.113332	20.677427	...	34.152966	95.329383	45157.810902	41.821374	0.249189	1.432389	0.566405	0.152414	0.130876	7.0
7	1	26	96	0	10.331387	2.498662	16.020874	0.100707	5.922602	7.850522	...	65.508907	75.616780	37165.463625	65.573652	1.780176	3.982989	0.620034	0.369095	0.722737	8.0
8	0	15	112	0	14.262607	8.888795	0.773295	2.281113	68.691901	0.827132	...	190.000000	28.773361	200000.000000	35.931179	0.290472	1.772127	0.087045	0.512316	0.922651	9.0
9	0	28	137	0	5.870684	0.824999	3.084907	11.425205	37.872839	19.419044	...	50.228614	89.822382	65233.389597	36.545510	0.035157	0.874001	0.708388	0.766209	0.142649	10.0
10	1	27	226	1	7.088535	0.283055	8.315342	1.366076	2.214913	41.120205	...	44.067410	37.340224	12190.119629	48.818017	0.138935	0.658657	0.788564	0.017558	0.923704	11.0
11	1	21	128	0	16.149193	3.850653	3.196988	8.597772	47.642021	29.893714	...	166.455950	10.524902	43574.175204	18.537058	7.382145	13.366764	0.948362	0.258017	0.969015	12.0
12	0	2	183	0	11.716227	1.283955	13.915028	12.128053	22.875005	1.569514	...	10.024603	113.904802	17217.333644	8.255863	1.179744	2.731058	0.230116	0.347973	0.650547	13.0
13	1	23	172	0	6.503985	0.096397	2.708102	30.000000	40.633315	2.772596	...	53.472655	33.181023	200000.000000	33.145742	0.619742	2.244286	0.810021	0.711795	0.392277	14.0
14	0	0	101	0	25.101203	3.188984	17.604631	0.099994	250.171717	8.930461	...	10.210870	112.839109	48378.428305	23.698705	1.241074	11.912208	0.778765	0.978677	0.859013	15.0
15	1	28	200	0	3.000000	6.272574	0.296121	6.216673	28.286259	16.845520	...	76.036535	60.189079	125226.666808	28.999614	1.578759	6.275846	0.387570	0.785484	0.732166	16.0
16	0	10	85	0	13.091479	2.178118	4.339566	0.919820	213.849629	7.779880	...	89.554035	72.450946	39052.915945	6.732413	0.494723	4.957723	0.581744	0.438515	0.525448	17.0
17	0	24	185	0	3.000000	3.727984	5.917777	4.932776	99.232097	17.134669	...	9.215928	50.604892	115951.083186	60.904464	13.264392	7.963693	0.662598	0.920679	0.150350	18.0
18	1	12	163	0	3.000000	0.153387	4.543918	2.796197	206.324350	11.374446	...	8.454184	6.669602	200000.000000	67.454921	5.150976	4.218149	0.431221	0.228147	0.994235	19.0
19	0	0	217	0	13.331367	2.774178	6.488598	3.763119	8.618444	1.289124	...	35.583323	8.314417	16382.942987	600.878815	12.867072	6.429398	0.450828	0.469069	0.654148	20.0