From bc546172d1dc5b511a80d174174d829a2e243cf4 Mon Sep 17 00:00:00 2001 From: Pranav Kodlinge Date: Fri, 4 Oct 2024 18:51:16 +0530 Subject: [PATCH] Added price prediction model This model is made by collecting official data from government websites and based on rainfall and temperature it will predict the future price of commodities. --- .../Data/Agmarknet_Price_Report (1).xls | 324 + .../Data/Agmarknet_Price_Report.xls | 323 + .../Data/MumbaiTur.xls | 14351 ++++++++++++++++ .../Data/MumbaiTurExcl.xlsx | Bin 0 -> 21388 bytes .../Data/Mumbai_masur.xlsx | Bin 0 -> 15717 bytes .../Data/Mumbai_moong.xlsx | Bin 0 -> 15659 bytes .../Data/Mumbai_urad_.xlsx | Bin 0 -> 13754 bytes .../Data/agrodelhhi.xlsx | Bin 0 -> 81976 bytes .../Mumbai_masur.py | 32 + .../Mumbai_moong.py | 32 + .../Mumbai_urad.py | 32 + models/Agriculture_price_prediction/app.py | 259 + 12 files changed, 15353 insertions(+) create mode 100644 models/Agriculture_price_prediction/Data/Agmarknet_Price_Report (1).xls create mode 100644 models/Agriculture_price_prediction/Data/Agmarknet_Price_Report.xls create mode 100644 models/Agriculture_price_prediction/Data/MumbaiTur.xls create mode 100644 models/Agriculture_price_prediction/Data/MumbaiTurExcl.xlsx create mode 100644 models/Agriculture_price_prediction/Data/Mumbai_masur.xlsx create mode 100644 models/Agriculture_price_prediction/Data/Mumbai_moong.xlsx create mode 100644 models/Agriculture_price_prediction/Data/Mumbai_urad_.xlsx create mode 100644 models/Agriculture_price_prediction/Data/agrodelhhi.xlsx create mode 100644 models/Agriculture_price_prediction/Mumbai_masur.py create mode 100644 models/Agriculture_price_prediction/Mumbai_moong.py create mode 100644 models/Agriculture_price_prediction/Mumbai_urad.py create mode 100644 models/Agriculture_price_prediction/app.py diff --git a/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report (1).xls b/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report (1).xls new file mode 100644 index 000000000..a831dfc6a --- /dev/null +++ b/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report (1).xls @@ -0,0 +1,324 @@ + + + + + + + + + + + + + + + + + + + + + + <body> + <p>This page uses frames, but your browser doesn't support them.</p> + </body> + + + diff --git a/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report.xls b/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report.xls new file mode 100644 index 000000000..3f7f6c947 --- /dev/null +++ b/models/Agriculture_price_prediction/Data/Agmarknet_Price_Report.xls @@ -0,0 +1,323 @@ + + + + + + + + + + + + + + + + + + + + + + <body> + <p>This page uses frames, but your browser doesn't support them.</p> + </body> + + + diff --git a/models/Agriculture_price_prediction/Data/MumbaiTur.xls b/models/Agriculture_price_prediction/Data/MumbaiTur.xls new file mode 100644 index 000000000..50802160c --- /dev/null +++ b/models/Agriculture_price_prediction/Data/MumbaiTur.xls @@ -0,0 +1,14351 @@ +Min,Max,Modal PriceMumbai from 01-Jan-2022To24-Aug-2024 (Total-652) (Total-652)

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Sl no.	District Name	Market Name	Commodity	Variety	Grade	Min Price (Rs./Quintal)	Max Price (Rs./Quintal)	Modal Price (Rs./Quintal)	Price Date
+ 1 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11500 +	+ 10000 +	+ 29 Dec 2022 +
+ 2 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 16500 +	+ 13500 +	+ 01 Nov 2023 +
+ 3 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 11500 +	+ 10200 +	+ 19 Oct 2022 +
+ 4 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 12 Aug 2022 +
+ 5 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9000 +	+ 14 Apr 2022 +
+ 6 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14700 +	+ 13 Mar 2024 +
+ 7 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17000 +	+ 14500 +	+ 27 Mar 2024 +
+ 8 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 28 Jun 2024 +
+ 9 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 9500 +	+ 8500 +	+ 19 Jan 2022 +
+ 10 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9000 +	+ 30 Apr 2022 +
+ 11 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11500 +	+ 10000 +	+ 25 Jan 2023 +
+ 12 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 24 Mar 2022 +
+ 13 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11500 +	+ 10000 +	+ 02 Jan 2023 +
+ 14 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 26 Aug 2023 +
+ 15 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 30 May 2023 +
+ 16 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 13500 +	+ 12500 +	+ 15 Jul 2023 +
+ 17 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14700 +	+ 24 Feb 2024 +
+ 18 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 06 Apr 2022 +
+ 19 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 29 Jun 2022 +
+ 20 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9000 +	+ 05 Feb 2022 +
+ 21 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14500 +	+ 26 Dec 2023 +
+ 22 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11000 +	+ 10000 +	+ 09 Mar 2023 +
+ 23 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 12500 +	+ 11500 +	+ 14 Jun 2023 +
+ 24 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 16500 +	+ 13500 +	+ 10 Nov 2023 +
+ 25 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 02 Aug 2022 +
+ 26 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 25 Aug 2023 +
+ 27 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11000 +	+ 10000 +	+ 11 Mar 2023 +
+ 28 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 13 Apr 2023 +
+ 29 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11500 +	+ 10000 +	+ 31 Dec 2022 +
+ 30 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11500 +	+ 10000 +	+ 04 Jan 2023 +
+ 31 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14500 +	+ 11 Dec 2023 +
+ 32 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 11000 +	+ 10000 +	+ 15 Sep 2022 +
+ 33 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 25 Mar 2023 +
+ 34 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17000 +	+ 14500 +	+ 08 Apr 2024 +
+ 35 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 03 Aug 2022 +
+ 36 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17000 +	+ 14500 +	+ 14 May 2024 +
+ 37 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 11000 +	+ 10000 +	+ 14 Feb 2023 +
+ 38 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 05 Mar 2022 +
+ 39 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 31 Mar 2022 +
+ 40 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 21 Mar 2023 +
+ 41 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14700 +	+ 20 Mar 2024 +
+ 42 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 26 May 2023 +
+ 43 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10000 +	+ 9000 +	+ 09 Jun 2022 +
+ 44 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 28 Mar 2022 +
+ 45 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 10 Aug 2023 +
+ 46 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 05 Aug 2022 +
+ 47 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 13500 +	+ 12500 +	+ 08 Jul 2023 +
+ 48 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9000 +	+ 03 Feb 2022 +
+ 49 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 02 Mar 2022 +
+ 50 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 02 Apr 2022 +
+ 51 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 9500 +	+ 8500 +	+ 24 Jan 2022 +
+ 52 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 12500 +	+ 11500 +	+ 16 Jun 2023 +
+ 53 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 01 Sep 2023 +
+ 54 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9200 +	+ 17000 +	+ 13500 +	+ 05 Jan 2024 +
+ 55 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 16 Jul 2024 +
+ 56 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9000 +	+ 20 Apr 2022 +
+ 57 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 11000 +	+ 10000 +	+ 10 Sep 2022 +
+ 58 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 23 Aug 2023 +
+ 59 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 07 Aug 2024 +
+ 60 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 12 Mar 2022 +
+ 61 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9000 +	+ 11 Apr 2022 +
+ 62 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 17500 +	+ 14000 +	+ 13 Aug 2024 +
+ 63 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 7500 +	+ 11500 +	+ 10500 +	+ 24 Apr 2023 +
+ 64 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9200 +	+ 17000 +	+ 13500 +	+ 03 Feb 2024 +
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+ 70 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9500 +	+ 14500 +	+ 13000 +	+ 21 Aug 2023 +
+ 71 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 9200 +	+ 17000 +	+ 13500 +	+ 17 Jan 2024 +
+ 72 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10500 +	+ 9500 +	+ 22 Mar 2022 +
+ 73 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 10000 +	+ 9000 +	+ 19 May 2022 +
+ 74 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 21 Jun 2022 +
+ 75 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 11500 +	+ 10200 +	+ 31 Oct 2022 +
+ 76 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 22 Jun 2022 +
+ 77 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9500 +	+ 12 Jul 2022 +
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+ 79 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 24 Jun 2024 +
+ 80 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 26 Jul 2024 +
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+ 99 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 12500 +	+ 11500 +	+ 23 Jun 2023 +
+ 100 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 16500 +	+ 13500 +	+ 09 Nov 2023 +
+ 101 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8000 +	+ 10500 +	+ 9000 +	+ 08 Feb 2022 +
+ 102 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 10500 +	+ 17500 +	+ 14700 +	+ 28 Feb 2024 +
+ 103 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8500 +	+ 11500 +	+ 10200 +	+ 15 Dec 2022 +
+ 104 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 11000 +	+ 17500 +	+ 14500 +	+ 07 Jun 2024 +
+ 105 +	+ Mumbai +	+ Mumbai +	+ Arhar Dal(Tur Dal) +	+ Other +	+ FAQ +	+ 8900 +	+ 11000 +	+ 10000 +	+ 13 Oct 2022 +
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zN;2eE#a<%b^1N7mi{dHn$)_A55puM-Q*0R9*NW-scxqW@rNl)Z4?QR4W+kwPc5w!r6BXE6;PWuElq&J9NA+x)h$*%9L)pe*a!L*tvymtu`R`0UbzqQAM81 zJMK6#bUo>eFEW~}!gmIIlGoS6=}>Xl@dZ}}8e{;LJne+5<$Fp*C%lzCwMDI4GLs)W zt&^V*bMJEl9W0Oq)5b6z<#T4c2s?;mLrxQwiLKUiQ~HdP1U=9 z%<^>R=yEF8k5v7-^wvs_26#V$habVkHq_rE=){_GTbpkrD7RCUnddaM;Fx3Z26P5z zdAJ06_ynmauRS*dkBzY*&p03j&KldX0r;3x!`U(j#s_-4orQ3k}<~tRdWc$ zHh_yci4P9=N5{WX{B{uO0r4+KM1cS*=Nx5`4Lt8SlleJ_AWshuLXgV$*Z0kf%~RRD z-?2`_mIMB7_v1eVn=&@*1R+Y_3D)yBo1vQ{l6B}SjUUj>@yTX}jSh7k4*=L}0RjJU lubbf;t>LfmUhQAtKW(G6IX6eT0Kj(6P2>oVidys2{{SImnQ{OC literal 0 HcmV?d00001 diff --git a/models/Agriculture_price_prediction/Mumbai_masur.py b/models/Agriculture_price_prediction/Mumbai_masur.py new file mode 100644 index 000000000..df5eba47b --- /dev/null +++ b/models/Agriculture_price_prediction/Mumbai_masur.py @@ -0,0 +1,32 @@ +import pandas as pd +from prophet import Prophet +import matplotlib.pyplot as plt + +# Step 1: Load Data from Excel +file_path = 'Mumbai_masur.xlsx' # Replace with your file path +df = pd.read_excel(file_path, engine='openpyxl') # Use xlrd for .xls files + +# Ensure the columns exist in the data +assert 'min_price' in df.columns, "min_price column is missing in the data" +assert 'max_price' in df.columns, "max_price column is missing in the data" +assert 'modal_price' in df.columns, "modal_price column is missing in the data" +assert 'date' in df.columns, "date column is missing in the data" + +# Step 2: Prepare the Data +df['date'] = pd.to_datetime(df['date'], format='%d-%b-%y') # Adjust format if needed +df = df[['date', 'modal_price']] +df.columns = ['ds', 'y'] # Rename columns for Prophet + +# Step 3: Initialize and Train Prophet Model +model = Prophet() +model.fit(df) + +# Step 4: Make Future DataFrame +future = model.make_future_dataframe(periods=90) # Adjust period as needed for future predictions + +# Step 5: Forecast +forecast = model.predict(future) + +# Step 6: Plot Results +fig = model.plot(forecast) +plt.show() \ No newline at end of file diff --git a/models/Agriculture_price_prediction/Mumbai_moong.py b/models/Agriculture_price_prediction/Mumbai_moong.py new file mode 100644 index 000000000..7b3f28163 --- /dev/null +++ b/models/Agriculture_price_prediction/Mumbai_moong.py @@ -0,0 +1,32 @@ +import pandas as pd +from prophet import Prophet +import matplotlib.pyplot as plt + +# Step 1: Load Data from Excel +file_path = 'Mumbai_moong.xlsx' # Replace with your file path +df = pd.read_excel(file_path, engine='openpyxl') # Use xlrd for .xls files + +# Ensure the columns exist in the data +assert 'min_price' in df.columns, "min_price column is missing in the data" +assert 'max_price' in df.columns, "max_price column is missing in the data" +assert 'modal_price' in df.columns, "modal_price column is missing in the data" +assert 'date' in df.columns, "date column is missing in the data" + +# Step 2: Prepare the Data +df['date'] = pd.to_datetime(df['date'], format='%d-%b-%y') # Adjust format if needed +df = df[['date', 'modal_price']] +df.columns = ['ds', 'y'] # Rename columns for Prophet + +# Step 3: Initialize and Train Prophet Model +model = Prophet() +model.fit(df) + +# Step 4: Make Future DataFrame +future = model.make_future_dataframe(periods=90) # Adjust period as needed for future predictions + +# Step 5: Forecast +forecast = model.predict(future) + +# Step 6: Plot Results +fig = model.plot(forecast) +plt.show() \ No newline at end of file diff --git a/models/Agriculture_price_prediction/Mumbai_urad.py b/models/Agriculture_price_prediction/Mumbai_urad.py new file mode 100644 index 000000000..0e7774c9b --- /dev/null +++ b/models/Agriculture_price_prediction/Mumbai_urad.py @@ -0,0 +1,32 @@ +import pandas as pd +from prophet import Prophet +import matplotlib.pyplot as plt + +# Step 1: Load Data from Excel +file_path = 'Mumbai_urad_.xlsx' # Replace with your file path +df = pd.read_excel(file_path, engine='openpyxl') # Use xlrd for .xls files + +# Ensure the columns exist in the data +assert 'min_price' in df.columns, "min_price column is missing in the data" +assert 'max_price' in df.columns, "max_price column is missing in the data" +assert 'modal_price' in df.columns, "modal_price column is missing in the data" +assert 'date' in df.columns, "date column is missing in the data" + +# Step 2: Prepare the Data +df['date'] = pd.to_datetime(df['date'], format='%d-%b-%y') # Adjust format if needed +df = df[['date', 'modal_price']] +df.columns = ['ds', 'y'] # Rename columns for Prophet + +# Step 3: Initialize and Train Prophet Model +model = Prophet() +model.fit(df) + +# Step 4: Make Future DataFrame +future = model.make_future_dataframe(periods=90) # Adjust period as needed for future predictions + +# Step 5: Forecast +forecast = model.predict(future) + +# Step 6: Plot Results +fig = model.plot(forecast) +plt.show() \ No newline at end of file diff --git a/models/Agriculture_price_prediction/app.py b/models/Agriculture_price_prediction/app.py new file mode 100644 index 000000000..ebb2d59e7 --- /dev/null +++ b/models/Agriculture_price_prediction/app.py @@ -0,0 +1,259 @@ +import streamlit as st +import pandas as pd +from prophet import Prophet +import plotly.graph_objects as go +from datetime import timedelta + +# Set the page configuration +st.set_page_config(page_title="Agricultural Price Forecasting", layout="wide") + +# Custom CSS for enhanced UI with background image and semi-transparent overlay +st.markdown( + """ + + """, + unsafe_allow_html=True +) + +# Add an overlay div +st.markdown('
', unsafe_allow_html=True) + +# Title with gradient and shadow +st.markdown("
Agricultural Price Forecasting
", unsafe_allow_html=True) +st.markdown( + "
Predict and Visualize Commodity Prices Based on Rainfall and Temperature
", + unsafe_allow_html=True) + +# Sidebar for user inputs with better organization +st.sidebar.markdown("
User Inputs
", unsafe_allow_html=True) + +with st.sidebar.expander("Select Parameters", expanded=True): + state = st.selectbox("Select State", ["Mumbai"], + help="Choose the state for which you want to forecast prices.") + commodity = st.selectbox("Select Commodity", ["Tur", "Moong", "Urad", "Masur"], + help="Choose the commodity to analyze.") + days_to_predict = st.slider("Select number of days to predict", min_value=1, max_value=60, value=30, + help="Select the number of days for price forecasting.") + rainfall = st.slider("Enter Rainfall (in inches)", min_value=0, max_value=600, value=25, + help="Enter the rainfall amount in inches.") + temperature = st.slider("Enter Temperature (in °C)", min_value=-5, max_value=50, value=20, + help="Enter the temperature in degrees Celsius.") + +# File paths +file_path_delhi = 'Data\\agrodelhhi.xlsx' # Replace with the path to your Excel file for Delhi +file_path_mumbai_tur = 'Data\\MumbaiTurExcl.xlsx' # Replace with the path to your Excel file for Mumbai Tur +file_path_mumbai_masur = 'Data\\Mumbai_masur.xlsx' # Replace with the path to your Excel file for Mumbai Masur +file_path_mumbai_moong = 'Data\\Mumbai_moong.xlsx' # Replace with the path to your Excel file for Mumbai Moong +file_path_mumbai_urad = 'Data\\Mumbai_urad_.xlsx' # Replace with the path to your Excel file for Mumbai Urad + + +# Load data +@st.cache_data +def load_data(file_path): + try: + df = pd.read_excel(file_path, engine='openpyxl') # Use openpyxl for .xlsx files + df['date'] = pd.to_datetime(df['date'], format='%d-%b-%y') # Adjust format if needed + return df + except FileNotFoundError: + st.error("Data file not found. Please check the file path.") + return None + except Exception as e: + st.error(f"An error occurred: {e}") + return None + + +# Prediction logic +def predict_prices(df, commodity, days_to_predict, rainfall, temperature): + # Prepare the Data for Prophet + df_prophet = df[['date', 'modal_price']].rename(columns={'date': 'ds', 'modal_price': 'y'}) + # Initialize and train the Prophet model + model = Prophet() + model.fit(df_prophet) + # Create a future dataframe for predictions + future = model.make_future_dataframe(periods=days_to_predict) + # Make predictions + forecast = model.predict(future) + + # Rename columns for clarity + forecast = forecast.rename(columns={'ds': 'date', 'yhat': 'Price Prediction'}) + + # Define optimal ranges for rainfall and temperature for each commodity + optimal_ranges = { + "Apple": {"rainfall": (30, 35), "temperature": (15, 25)}, + "Tur": {"rainfall": (25, 35), "temperature": (20, 30)}, + "Moong": {"rainfall": (20, 30), "temperature": (25, 35)}, + "Urad": {"rainfall": (20, 30), "temperature": (25, 35)}, + "Masur": {"rainfall": (20, 30), "temperature": (20, 30)}, + } + + # Apply rainfall and temperature-based price adjustment only to forecasted data + if commodity in optimal_ranges: + # Get the optimal ranges + optimal_rainfall = optimal_ranges[commodity]["rainfall"] + optimal_temperature = optimal_ranges[commodity]["temperature"] + + # Forecast adjustments + future_dates = forecast['date'] + + # Rainfall adjustments + if rainfall < optimal_rainfall[0]: # Below Essential Range + adjustment_factor = 1 + (optimal_rainfall[0] - rainfall) * 0.01 + adjustment_factor = min(adjustment_factor, 1.3) # Cap at 30% increase + elif rainfall > optimal_rainfall[1]: # Above Essential Range + adjustment_factor = 1 + (rainfall - optimal_rainfall[1]) * 0.01 + adjustment_factor = min(adjustment_factor, 1.3) # Cap at 30% increase + else: # Within Essential Range + adjustment_factor = 0.95 # Decrease price slightly by 5% + + # Temperature adjustments + if optimal_temperature[0] <= temperature <= optimal_temperature[1]: # Optimal temperature range + temperature_adjustment = 0.95 # Decrease price slightly by 5% + else: + # Outside optimal range, increase prices slightly (simulate reduced production) + if temperature < optimal_temperature[0]: + temperature_adjustment = 1 + (optimal_temperature[0] - temperature) * 0.02 # Mild increase + elif temperature > optimal_temperature[1]: + temperature_adjustment = 1 + (temperature - optimal_temperature[1]) * 0.02 # Mild increase + temperature_adjustment = min(temperature_adjustment, 1.3) # Cap at 30% increase + + # Apply both adjustments to the forecast + forecast.loc[ + forecast['date'] > df['date'].max(), 'Price Prediction'] *= adjustment_factor * temperature_adjustment + + return forecast + + +# Show the data visualization and predictions only after the user clicks "Submit" +if st.sidebar.button("Submit"): + # Determine the file path based on user inputs + if state == "Delhi" and commodity == "Apple": + df = load_data(file_path_delhi) + elif state == "Mumbai": + if commodity == "Tur": + df = load_data(file_path_mumbai_tur) + elif commodity == "Masur": + df = load_data(file_path_mumbai_masur) + elif commodity == "Moong": + df = load_data(file_path_mumbai_moong) + elif commodity == "Urad": + df = load_data(file_path_mumbai_urad) + else: + df = None + + if df is not None: + # Apply prediction logic + forecast = predict_prices(df, commodity, days_to_predict, rainfall, temperature) + + # Plot results using Plotly + fig = go.Figure() + + # Historical data + fig.add_trace(go.Scatter(x=df['date'], y=df['modal_price'], mode='markers', name='Historical Price', + marker=dict(color='blue', size=8))) + + # Forecasted price + fig.add_trace( + go.Scatter(x=forecast['date'], y=forecast['Price Prediction'], mode='lines', name='Forecasted Price', + line=dict(color='red'))) + + # Add trendlines and seasonality (if applicable) + fig.update_layout(title=f'{commodity} Price Forecast', + xaxis_title='Date', + yaxis_title='Price', + template='plotly_white') + + # Remove numbering from date and format date + fig.update_xaxes(tickformat='%Y-%m-%d') + + # Show plot + st.plotly_chart(fig, use_container_width=True) + + # Show future predictions + st.write(f"Predicted prices for the next {days_to_predict} days:") + st.write(forecast[['date', 'Price Prediction']].tail(days_to_predict)) + + # Export the forecast to a CSV file and provide a download link + csv = forecast[['date', 'Price Prediction']].to_csv(index=False) + st.download_button( + label="Download Forecast Data as CSV", + data=csv, + file_name=f"{commodity}_forecast.csv", + mime="text/csv", + ) + + # Add feedback form + # st.write("**Feedback:**") + # feedback = st.text_area("Please provide your feedback or suggestions:") + # if st.button("Submit Feedback"): + # st.write("Thank you for your feedback!") + +# Footer +st.markdown("
© 2024 Developed by Stunning_Hunters.Agricultural Price Forecasting. All rights reserved.
", unsafe_allow_html=True)