#Importing libraries

import pandas as pd
import matplotlib.pyplot as plt

Importing Datasets

rent = pd.read_csv('NCR Rent Survey.csv')
print('NCR Rent Survey')
rent

cars = pd.read_csv('Car Users Survey.csv')
print('NCR Car Users Survey')
cars

NCR Car Users Survey

	Timestamp	First Name	Workplace Distance	Monthly Consumption (L)
0	10/29/23 16:12	Josef	12	75
1	10/29/23 16:37	Martin	15	100
2	10/29/23 16:45	Francis	22	150
3	10/29/23 18:06	Chito	18	120
4	10/29/23 20:25	Gia	15	100
5	10/29/23 22:44	Nicky	17	110
6	10/29/23 22:55	Anjo	18	120
7	10/30/23 12:18	Marcus	10	70
8	10/30/23 12:31	Rina	25	175
9	10/30/23 12:44	Josh	13	85
10	10/30/23 12:47	Albert	18	120
11	10/30/23 12:49	Gerard	18	120
12	10/30/23 13:05	Odie	15	100
13	10/30/23 13:15	Jason	14	100
14	10/30/23 13:18	Luis	20	130
15	10/30/23 13:21	Raymond	18	120
16	10/30/23 13:24	Marie	15	100
17	10/30/23 13:27	Elmer	10	70
18	10/30/23 13:35	Ruffa	15	100
19	10/30/23 13:50	Alma	12	80
20	10/30/23 14:23	Eddie	10	70
21	10/30/23 14:42	Tommy	15	100
22	10/30/23 15:00	Rolando	17	120
23	10/30/23 15:05	Amery	23	160
24	10/30/23 15:07	Earvin	16	110
25	10/30/23 15:21	Sheena	13	90
26	10/30/23 15:22	Angelica	10	70
27	10/30/23 16:27	Robert	15	100
28	10/30/23 16:53	Dino	30	200
29	10/30/23 17:08	Cyron	24	160
30	10/30/23 19:50	Patrick	21	140
31	10/30/23 20:15	Francheska	14	100
32	10/30/23 21:48	Wendy	18	120
33	10/31/23 9:15	Holly	22	150
34	10/31/23 9:33	Ivan	17	120
35	10/31/23 9:42	Nathan	23	160
36	10/31/23 10:08	Brylle	15	100
37	10/31/23 10:27	Neil	25	170
38	10/31/23 10:36	Zion	16	110
39	10/31/23 11:43	Jacob	14	100
40	10/31/23 11:55	Marissa	18	120
41	10/31/23 12:21	Charles	13	90
42	10/31/23 13:36	Gary	18	120
43	10/31/23 14:12	Bernardo	20	140
44	10/31/23 14:49	Sebastian	21	140
45	10/31/23 15:08	Ella	15	100
46	10/31/23 15:52	Michael	18	120
47	10/31/23 16:19	Julius	25	180
48	10/31/23 16:48	Ariel	28	200
49	10/31/23 17:21	Papa G	30	200

traffic = pd.read_csv('MMDA Dataset.csv')
print('Annual Average Daily Traffic - MMDA')
traffic

Annual Average Daily Traffic - MMDA

	Year	Car	PUV	UV	Taxi	Pub	Truck	Trailer	MC	Tricycle
0	2021	1399242	73766	25805	130855	24693	84738	18477	1421642	18455
1	2022	1563069	100876	33639	125008	23412	74942	20030	1573729	21050

gasprice = pd.read_csv('gasprice.csv')
print('Gasoline Price: 2020-2023')
gasprice

Gasoline Price: 2020-2023

	Date	Gasoline Price
0	1-Nov-20	53.6064
1	1-Dec-20	54.1648
2	1-Jan-21	56.9568
3	1-Feb-21	60.3072
4	1-Mar-21	59.1904
5	1-Apr-21	58.6320
6	1-May-21	60.8656
7	1-Jun-21	62.5408
8	1-Jul-21	63.6576
9	1-Aug-21	63.0992
10	1-Sep-21	57.5152
11	1-Oct-21	70.3584
12	1-Nov-21	69.8000
13	1-Dec-21	67.5664
14	1-Jan-22	72.5920
15	1-Feb-22	69.8000
16	1-Mar-22	79.8512
17	1-Apr-22	78.7344
18	1-May-22	86.5520
19	1-Jun-22	86.5520
20	1-Jul-22	75.3840
21	1-Aug-22	73.1504
22	1-Sep-22	63.0992
23	1-Oct-22	64.7744
24	1-Nov-22	68.1248
25	1-Dec-22	63.0992
26	1-Jan-23	69.8000
27	1-Feb-23	69.2416
28	1-Mar-23	63.0992
29	1-Apr-23	64.7744
30	1-May-23	58.6320
31	1-Jun-23	60.3072
32	1-Jul-23	63.6576
33	1-Aug-23	68.1248
34	1-Sep-23	67.5664
35	1-Oct-23	62.5408

Analysis

Rent

rentcity = rent.groupby('City')
averagerentcity = rentcity['Rent Fee'].mean()
averagerentcity = averagerentcity.sort_values(ascending=False)

fig, ax = plt.subplots()
bars = ax.bar(averagerentcity.index, averagerentcity, color='skyblue', edgecolor='black')

for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2., height, str(round(height, 0)),
            ha='center', va='top', rotation=90)

plt.title('Average Rent Fee by City')
plt.xlabel('Room Type')
plt.ylabel('Rent Fee')
plt.xticks(rotation=90)

fig.tight_layout()

plt.show()

renttype = rent.groupby('Room Type')
averagerenttype = renttype['Rent Fee'].mean()

fig, ax = plt.subplots()
bars = ax.bar(averagerenttype.index, averagerenttype, color='skyblue', edgecolor='black')

for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2., height, str(round(height, 0)),
            ha='center', va='bottom')

plt.title('Average Rent Fee by Room Type')
plt.xlabel('Room Type')
plt.ylabel('Rent Fee')

fig.tight_layout()

plt.show()

rent['Commute Cost per Month'] = rent['Commute Cost per Day'] * 21

rent['Variance'] = rent['Commute Cost per Month'] - rent['Rent Fee']

rent

	Timestamp	Name	City	Rent Fee	Room Type	Commute Cost per Day	Commute Cost per Month	Variance
0	10/28/23 16:37	James	Makati	15000	Condo	700	14700	-300
1	10/28/23 16:37	Marcus Sy	Pasig	7000	Apartment	500	10500	3500
2	10/28/23 16:45	Chloe	Quezon City	6000	Apartment	300	6300	300
3	10/28/23 18:06	Maryel	Makati	4000	Bedspace	300	6300	2300
4	10/28/23 20:25	Eleine	Pasay	4500	Bedspace	250	5250	750
5	10/28/23 22:44	Aly	Makati	7000	Apartment	350	7350	350
6	10/29/23 17:55	Jose Santos	Taguig	9000	Condo	300	6300	-2700
7	10/30/23 12:18	Gemma	Taguig	4000	Bedspace	200	4200	200
8	10/30/23 12:31	Ruth	Makati	6000	Apartment	250	5250	-750
9	10/30/23 12:44	Anton	Quezon City	7500	Condo	300	6300	-1200
10	10/30/23 12:47	Vee	Manila	4000	Dorm	200	4200	200
11	10/30/23 12:49	Cookie Palad	Mandaluyong	12000	Condo	450	9450	-2550
12	10/30/23 13:05	Joanna	Makati	4000	Bedspace	300	6300	2300
13	10/30/23 13:15	Raffy	Paranaque	5000	Apartment	250	5250	250
14	10/30/23 13:18	David Mendoza	Manila	4500	Dorm	300	6300	1800
15	10/30/23 13:21	Sans	Pasay	5500	Apartment	300	6300	800
16	10/30/23 13:24	Antonio Flores	Makati	4500	Bedspace	250	5250	750
17	10/30/23 13:27	RC	Makati	6000	Apartment	300	6300	300
18	10/30/23 13:35	Pims	Taguig	12000	Condo	450	9450	-2550
19	10/30/23 13:50	Elery	Mandaluyong	5500	Apartment	200	4200	-1300
20	10/30/23 14:23	Bea	Las Pinas	5000	Apartment	350	7350	2350
21	10/30/23 14:42	Geo Deloso	Manila	10000	Condo	300	6300	-3700
22	10/30/23 15:00	Ed Bautista	San Juan	5000	Apartment	300	6300	1300
23	10/30/23 15:05	Nessy	Taguig	12000	Condo	400	8400	-3600
24	10/30/23 15:07	Don	Mandaluyong	10000	Condo	300	6300	-3700
25	10/30/23 15:21	Adonis	Pasay	4500	Apartment	250	5250	750
26	10/30/23 15:22	Hershie	Caloocan	5000	Apartment	200	4200	-800
27	10/30/23 16:27	Jessica Ferrer	Taguig	4000	Bedspace	300	6300	2300
28	10/30/23 16:53	Deah	Makati	12000	Condo	350	7350	-4650
29	10/30/23 17:08	Marky	Manila	3500	Bedspace	200	4200	700
30	10/30/23 19:50	Oliver	Pasig	10000	Condo	450	9450	-550
31	10/30/23 20:15	Tina	Taguig	4000	Bedspace	300	6300	2300
32	10/30/23 21:48	Marco Lagman	Pasay	3500	Bedspace	250	5250	1750
33	10/31/23 18:15	Yeshua	Mandaluyong	6000	Apartment	350	7350	1350
34	10/31/23 19:07	Zyra Leah	Quezon City	5500	Apartment	300	6300	800
35	10/31/23 19:07	Caren	Makati	7000	Apartment	250	5250	-1750
36	10/31/23 19:51	Jasper Go	Manila	3500	Bedspace	300	6300	2800
37	11/1/23 8:05	Howell	Makati	4000	Bedspace	250	5250	1250
38	11/1/23 11:19	Valerie	Quezon City	5500	Apartment	200	4200	-1300
39	11/1/23 20:56	Martin Reyes	Taguig	11000	Condo	300	6300	-4700
40	11/2/23 14:06	Penne	Makati	10000	Condo	350	7350	-2650
41	11/2/23 23:25	Micah	Manila	9000	Condo	300	6300	-2700
42	11/3/23 8:15	Rey Magno	Quezon City	6000	Apartment	250	5250	-750
43	11/3/23 8:19	Soy	Manila	5500	Apartment	250	5250	-250
44	11/3/23 9:31	Franky	San Juan	5000	Apartment	300	6300	1300
45	11/3/23 9:44	Inaki Lim	Quezon City	9000	Condo	400	8400	-600
46	11/3/23 11:56	Mel	Makati	4500	Bedspace	350	7350	2850
47	11/3/23 13:08	Rob Mojica	Quezon City	10000	Condo	300	6300	-3700
48	11/3/23 14:26	Gina Marie	Makati	14000	Condo	400	8400	-5600
49	11/3/23 15:19	Joel	Taguig	11000	Condo	600	12600	1600

negative_values = rent['Variance'][rent['Variance'] < 0]

negative_value_counts = negative_values.value_counts()

print("Amounts Exceeding Fares")
print(negative_value_counts)

Amounts Exceeding Fares
-3700    3
-750     2
-2550    2
-1300    2
-2700    2
-300     1
-1750    1
-600     1
-250     1
-2650    1
-4700    1
-800     1
-550     1
-4650    1
-3600    1
-1200    1
-5600    1
Name: Variance, dtype: int64

negative_count = (rent['Variance'] < 0).sum()

print(f'Number of renters who pays rent higher than his commute fares: {negative_count}')

Number of renters who pays rent higher than his commute fares: 23

negative_values = rent['Variance'][rent['Variance'] < -2000]

negative_value_counts = negative_values.value_counts()

print("Amounts Exceeding Fares")
print(negative_value_counts)

Amounts Exceeding Fares
-3700    3
-2700    2
-2550    2
-3600    1
-4650    1
-4700    1
-2650    1
-5600    1
Name: Variance, dtype: int64

rent.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 8 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   Timestamp               50 non-null     object
 1   Name                    50 non-null     object
 2   City                    50 non-null     object
 3   Rent Fee                50 non-null     int64 
 4   Room Type               50 non-null     object
 5   Commute Cost per Day    50 non-null     int64 
 6   Commute Cost per Month  50 non-null     int64 
 7   Variance                50 non-null     int64 
dtypes: int64(4), object(4)
memory usage: 3.3+ KB

renttype = rent.groupby('Room Type')
averagerentvar = renttype['Variance'].mean()

fig, ax = plt.subplots()
bars = ax.bar(averagerentvar.index, averagerentvar, color='skyblue', edgecolor='black')

for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2., height, str(round(height, 0)),
            ha='center', va='bottom')

plt.title('Average Variance by Room Type')
plt.xlabel('Room Type')
plt.ylabel('Fee Variance')

fig.tight_layout()

plt.show()

Car Users

cars.describe()

	Workplace Distance	Monthly Consumption (L)
count	50.000000	50.000000
mean	17.680000	120.100000
std	4.983401	34.396992
min	10.000000	70.000000
25%	15.000000	100.000000
50%	17.000000	120.000000
75%	20.750000	140.000000
max	30.000000	200.000000

#Use Jan 2024 gasoline price, compute for the  monthlycost

cars['Monthly Cost (Jan 2024)'] = cars['Monthly Consumption (L)'] * 69.7
cars

	Timestamp	First Name	Workplace Distance	Monthly Consumption (L)	Monthly Cost (Jan 2024)
0	10/29/23 16:12	Josef	12	75	5227.5
1	10/29/23 16:37	Martin	15	100	6970.0
2	10/29/23 16:45	Francis	22	150	10455.0
3	10/29/23 18:06	Chito	18	120	8364.0
4	10/29/23 20:25	Gia	15	100	6970.0
5	10/29/23 22:44	Nicky	17	110	7667.0
6	10/29/23 22:55	Anjo	18	120	8364.0
7	10/30/23 12:18	Marcus	10	70	4879.0
8	10/30/23 12:31	Rina	25	175	12197.5
9	10/30/23 12:44	Josh	13	85	5924.5
10	10/30/23 12:47	Albert	18	120	8364.0
11	10/30/23 12:49	Gerard	18	120	8364.0
12	10/30/23 13:05	Odie	15	100	6970.0
13	10/30/23 13:15	Jason	14	100	6970.0
14	10/30/23 13:18	Luis	20	130	9061.0
15	10/30/23 13:21	Raymond	18	120	8364.0
16	10/30/23 13:24	Marie	15	100	6970.0
17	10/30/23 13:27	Elmer	10	70	4879.0
18	10/30/23 13:35	Ruffa	15	100	6970.0
19	10/30/23 13:50	Alma	12	80	5576.0
20	10/30/23 14:23	Eddie	10	70	4879.0
21	10/30/23 14:42	Tommy	15	100	6970.0
22	10/30/23 15:00	Rolando	17	120	8364.0
23	10/30/23 15:05	Amery	23	160	11152.0
24	10/30/23 15:07	Earvin	16	110	7667.0
25	10/30/23 15:21	Sheena	13	90	6273.0
26	10/30/23 15:22	Angelica	10	70	4879.0
27	10/30/23 16:27	Robert	15	100	6970.0
28	10/30/23 16:53	Dino	30	200	13940.0
29	10/30/23 17:08	Cyron	24	160	11152.0
30	10/30/23 19:50	Patrick	21	140	9758.0
31	10/30/23 20:15	Francheska	14	100	6970.0
32	10/30/23 21:48	Wendy	18	120	8364.0
33	10/31/23 9:15	Holly	22	150	10455.0
34	10/31/23 9:33	Ivan	17	120	8364.0
35	10/31/23 9:42	Nathan	23	160	11152.0
36	10/31/23 10:08	Brylle	15	100	6970.0
37	10/31/23 10:27	Neil	25	170	11849.0
38	10/31/23 10:36	Zion	16	110	7667.0
39	10/31/23 11:43	Jacob	14	100	6970.0
40	10/31/23 11:55	Marissa	18	120	8364.0
41	10/31/23 12:21	Charles	13	90	6273.0
42	10/31/23 13:36	Gary	18	120	8364.0
43	10/31/23 14:12	Bernardo	20	140	9758.0
44	10/31/23 14:49	Sebastian	21	140	9758.0
45	10/31/23 15:08	Ella	15	100	6970.0
46	10/31/23 15:52	Michael	18	120	8364.0
47	10/31/23 16:19	Julius	25	180	12546.0
48	10/31/23 16:48	Ariel	28	200	13940.0
49	10/31/23 17:21	Papa G	30	200	13940.0

cars.describe()

	Workplace Distance	Monthly Consumption (L)	Monthly Cost (Jan 2024)
count	50.000000	50.000000	50.000000
mean	17.680000	120.100000	8370.970000
std	4.983401	34.396992	2397.470345
min	10.000000	70.000000	4879.000000
25%	15.000000	100.000000	6970.000000
50%	17.000000	120.000000	8364.000000
75%	20.750000	140.000000	9758.000000
max	30.000000	200.000000	13940.000000

carsfuel = cars.groupby('Monthly Consumption (L)')
carsfuelcost = carsfuel['Monthly Cost (Jan 2024)'].mean()
carsfuelcost = carsfuelcost.sort_values(ascending=False)

carsfuelcost

Monthly Consumption (L)
200    13940.0
180    12546.0
175    12197.5
170    11849.0
160    11152.0
150    10455.0
140     9758.0
130     9061.0
120     8364.0
110     7667.0
100     6970.0
90      6273.0
85      5924.5
80      5576.0
75      5227.5
70      4879.0
Name: Monthly Cost (Jan 2024), dtype: float64

fig, ax = plt.subplots()
bars = ax.bar(carsfuelcost.index, carsfuelcost, color='skyblue', edgecolor='black')

for bar in bars:
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2., height, str(round(height, 0)),
            ha='right', va='top', rotation=90)

plt.title('Average Fuel Cost by Monthly Consumption')
plt.xlabel('Liters')
plt.ylabel('Fuel Cost')
plt.xticks(rotation=90)

fig.tight_layout()

plt.show()

Gasoline Price (ARIMA)

# Convert the date column to datetime type
gasprice['Date'] = pd.to_datetime(gasprice['Date'])

# Plot the time series
plt.figure(figsize=(12, 6))
plt.plot(gasprice['Date'], gasprice['Gasoline Price'], label='Gasoline Price')
plt.title('Gasoline Price Over Time')
plt.xlabel('Date')
plt.ylabel('Gasoline Price')
plt.legend()
plt.show()

from statsmodels.tsa.seasonal import seasonal_decompose

# Decompose the time series
decomposition = seasonal_decompose(gasprice.set_index('Date')['Gasoline Price'], model='additive')

trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

# Plot the decomposed components
plt.figure(figsize=(12, 8))
plt.subplot(411)
plt.plot(gasprice['Date'], gasprice['Gasoline Price'], label='Original')
plt.legend()
plt.subplot(412)
plt.plot(gasprice['Date'], trend, label='Trend')
plt.legend()
plt.subplot(413)
plt.plot(gasprice['Date'], seasonal, label='Seasonal')
plt.legend()
plt.subplot(414)
plt.plot(gasprice['Date'], residual, label='Residual')
plt.legend()
plt.tight_layout()
plt.show()

from statsmodels.tsa.arima.model import ARIMA

# Fit ARIMA model
model = ARIMA(gasprice['Gasoline Price'], order=(1, 1, 1))  # Set appropriate values for p, d, and q
fitted_model = model.fit()

# Print model summary
print(fitted_model.summary())

                               SARIMAX Results                                
==============================================================================
Dep. Variable:         Gasoline Price   No. Observations:                   36
Model:                 ARIMA(1, 1, 1)   Log Likelihood                -105.515
Date:                Thu, 16 Nov 2023   AIC                            217.031
Time:                        04:43:00   BIC                            221.697
Sample:                             0   HQIC                           218.641
                                 - 36                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.2471      0.731     -0.338      0.735      -1.679       1.185
ma.L1          0.0662      0.698      0.095      0.924      -1.301       1.434
sigma2        24.3027      6.186      3.928      0.000      12.178      36.428
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 0.28
Prob(Q):                              0.96   Prob(JB):                         0.87
Heteroskedasticity (H):               0.98   Skew:                            -0.18
Prob(H) (two-sided):                  0.98   Kurtosis:                         3.27
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

# Forecast future values
forecast_steps = 12  # Adjust the number of steps as needed
forecast = fitted_model.get_forecast(steps=forecast_steps)
forecast_index = pd.date_range(gasprice['Date'].max() + pd.DateOffset(months=1), periods=forecast_steps, freq='M')
forecast_values = forecast.predicted_mean

# Plot the original data and forecasted values
plt.plot(gasprice['Date'], gasprice['Gasoline Price'], label='Historical Data')
plt.plot(forecast_index, forecast_values, label='Forecasted Values', linestyle='dashed')
plt.title('Gasoline Price Forecast')
plt.xlabel('Date')
plt.ylabel('Gasoline Price')
plt.xticks(rotation=90)
plt.legend()
plt.show()

from statsmodels.tsa.arima.model import ARIMA

# Fit ARIMA model
model = ARIMA(gasprice['Gasoline Price'], order=(1, 1, 1))  # Set appropriate values for p, d, and q
fitted_model = model.fit()

# Forecast future values
forecast_steps = 12  # Adjust the number of steps as needed
forecast = fitted_model.get_forecast(steps=forecast_steps)
forecast_index = pd.date_range(gasprice['Date'].max() + pd.DateOffset(months=1), periods=forecast_steps, freq='M')
forecast_values = forecast.predicted_mean
confidence_intervals = forecast.conf_int()  # Get confidence intervals

# Display the forecasted values and confidence intervals
forecast_df = pd.DataFrame({
    'Date': forecast_index,
    'Forecasted Price': forecast_values
})

print(forecast_df)

         Date  Forecasted Price
36 2023-11-30         63.439728
37 2023-12-31         63.217596
38 2024-01-31         63.272487
39 2024-02-29         63.258923
40 2024-03-31         63.262275
41 2024-04-30         63.261446
42 2024-05-31         63.261651
43 2024-06-30         63.261601
44 2024-07-31         63.261613
45 2024-08-31         63.261610
46 2024-09-30         63.261611
47 2024-10-31         63.261611

Gasoline Price (FORECAST in Excel)

gasforecast = pd.read_excel('gaspricemod.xlsx')

gasforecast = gasforecast.drop(index=range(0, 36))
gasforecast

	Date	Gasoline Price
36	2023-11-01	63.271609
37	2023-12-01	69.526763
38	2024-01-01	69.702851
39	2024-02-01	69.878938
40	2024-03-01	70.043665
41	2024-04-01	70.219752
42	2024-05-01	70.390159
43	2024-06-01	70.566246
44	2024-07-01	70.736654
45	2024-08-01	70.912741
46	2024-09-01	71.088828
47	2024-10-01	71.259235
48	2024-11-01	71.435323
49	2024-12-01	71.605730
50	2025-01-01	71.781817

gasforecasted = pd.read_excel('gaspriceforecasted.xlsx')
gasforecasted

	Date	Gasoline Price	Forecasted Price
0	2020-11-01	53.6064	NaN
1	2020-12-01	54.1648	NaN
2	2021-01-01	56.9568	NaN
3	2021-02-01	60.3072	NaN
4	2021-03-01	59.1904	NaN
5	2021-04-01	58.6320	NaN
6	2021-05-01	60.8656	NaN
7	2021-06-01	62.5408	NaN
8	2021-07-01	63.6576	NaN
9	2021-08-01	63.0992	NaN
10	2021-09-01	57.5152	NaN
11	2021-10-01	70.3584	NaN
12	2021-11-01	69.8000	NaN
13	2021-12-01	67.5664	NaN
14	2022-01-01	72.5920	NaN
15	2022-02-01	69.8000	NaN
16	2022-03-01	79.8512	NaN
17	2022-04-01	78.7344	NaN
18	2022-05-01	86.5520	NaN
19	2022-06-01	86.5520	NaN
20	2022-07-01	75.3840	NaN
21	2022-08-01	73.1504	NaN
22	2022-09-01	63.0992	NaN
23	2022-10-01	64.7744	NaN
24	2022-11-01	68.1248	NaN
25	2022-12-01	63.0992	NaN
26	2023-01-01	69.8000	NaN
27	2023-02-01	69.2416	NaN
28	2023-03-01	63.0992	NaN
29	2023-04-01	64.7744	NaN
30	2023-05-01	58.6320	NaN
31	2023-06-01	60.3072	NaN
32	2023-07-01	63.6576	NaN
33	2023-08-01	68.1248	NaN
34	2023-09-01	67.5664	NaN
35	2023-10-01	62.5408	NaN
36	2023-11-01	NaN	63.271609
37	2023-12-01	NaN	69.526763
38	2024-01-01	NaN	69.702851
39	2024-02-01	NaN	69.878938
40	2024-03-01	NaN	70.043665
41	2024-04-01	NaN	70.219752
42	2024-05-01	NaN	70.390159
43	2024-06-01	NaN	70.566246
44	2024-07-01	NaN	70.736654
45	2024-08-01	NaN	70.912741
46	2024-09-01	NaN	71.088828
47	2024-10-01	NaN	71.259235
48	2024-11-01	NaN	71.435323
49	2024-12-01	NaN	71.605730
50	2025-01-01	NaN	71.781817

from datetime import datetime

# Convert 'Date' column to datetime format
gasforecasted['Date'] = pd.to_datetime(gasforecasted['Date'], format='%d-%b-%y')

# Plotting
plt.figure(figsize=(10, 6))

# Plot Gasoline Price
plt.plot(gasforecasted['Date'], gasforecasted['Gasoline Price'], color='blue', label='Gasoline Price')

# Plot Forecasted Price
plt.plot(gasforecasted['Date'], gasforecasted['Forecasted Price'], color='orange', linestyle='dashed', label='Forecasted Price')

# Labeling and formatting
plt.title('Gasoline Price and Forecasted Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid(True)
plt.show()

Traffic

traffic

	Year	Car	PUV	UV	Taxi	Pub	Truck	Trailer	MC	Tricycle
0	2021	1399242	73766	25805	130855	24693	84738	18477	1421642	18455
1	2022	1563069	100876	33639	125008	23412	74942	20030	1573729	21050